Search Results

12th Women in Machine Learning Workshop (WiML 2017) The 12th WiML Workshop is co-located with NIPS in Long Beach, California on Monday, December 4th and Thursday, December 7th, 2017. Speakers Logistics Program Call for Participation Committee FAQ Code Of Conduct Machine learning is one of the fastest growing areas of computer science research. Search engines, text mining, social media analytics, face recognition, DNA sequence analysis, speech and handwriting recognition, healthcare analytics are just some of the applications in which machine learning is routinely used. In spite of the wide reach of machine learning and the variety of theory and applications, it covers, the percentage of female researchers is lower than in many other areas of computer science. Most women working in machine learning rarely get the chance to interact with other female researchers, making it easy to feel isolated and hard to find role models. The annual Women in Machine Learning Workshop is the flagship event of Women in Machine Learning . This technical workshop gives female faculty, research scientists, and graduate students in the machine learning community an opportunity to meet, network and exchange ideas, participate in career-focused panel discussions with senior women in industry and academia and learn from each other. Underrepresented minorities and undergraduates interested in machine learning research are encouraged to attend. We welcome all genders; however, any formal presentations, i.e. talks and posters, are given by women. We strive to create an atmosphere in which participants feel comfortable to engage in technical and career-related conversations. Now in its 12th year, the 2017 workshop is co-located with NIPS in Long Beach, California. A History of WiML poster was created to celebrate the 10th workshop , held in 2015 in Montreal, Canada 2015. Besides this un-workshop and annual workshop which is co-located with NeurIPS, Women in Machine Learning also organizes events such as breakfast at ICML and AAAI conferences and local meetup events, maintains a public directory of women active in ML, profiles the research of women in ML, and maintains a list of resources for women working in ML. Invited Speakers Jenn Wortman Vaughan Senior Researcher at Microsoft Research Raia Hadsell DeepMind Tamara Broderick TT Career Development Assistant Professor in the Department of Electrical Engineering and Computer Science at MIT Hannah Wallach Senior Researcher at Microsoft Research New York City and an Adjunct Associate Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst Joelle Pineau Professor of Computer Science at McGill University where she co-directs the Reasoning and Learning Lab Nina Mishra Principal Scientist at Amazon and a visiting scholar at Stanford Location Co-located with NIPS in Long Beach, California. This workshop takes place in Long Beach Convention Center . PROGRAM MENTORSHIP ROUNDTABLES (MONDAY) MENTORSHIP ROUNDTABLES (THURSDAY) POSTERS Sunday 12:00 – 14:00 Registration Desk Opens 19:00 – 22:30 Pre Workshop Dinner (Optional). Separate Registration Required Monday All events are held in Room 104, except for the poster session, which takes place in the Pacific Ballroom 9:00 – 12:00 Registration Desk Opens 11:00 – 11:15 Opening Remarks – Jenn Wortman Vaughan Microsoft Research. Co-Founder of WiML. 11:15 – 11:50 Invited Talk – Tamara Broderick MIT. Bayesian machine learning: Quantifying uncertainty and robustness at scale 11:50 – 12:10 Contributed Talk: Aishwarya Unnikrishnan, Indraprastha Institute of Information Technology Delhi. Towards Communication-Centric Multi-Agent Deep Reinforcement Learning for Guarding a Territory Game. 12:10 – 12:30 Contributed Talk: Peyton Greenside, Stanford University. Graph convolutional networks can encode three-dimensional genome architecture in deep learning models for genomics. 12:30 – 14:00 Lunch + Poster Session 14:00 – 14:35 Invited Talk – Hanna Wallach Microsoft Research. Machine Learning for Social Science 14:35 – 14:50 Coffee Break 14:50 – 15:50 Research and Career Advice Roundtables 15:55 – 16:15 Contributed Talk: Palak Agarwal, WorldQuant. Fairness Aware Recommendations. 16:15 – 16:35 Contributed Talk: Victoria Krakovna, DeepMind. Reinforcement Learning with a Corrupted Reward Channel. 16:35 – 16:45 Closing Remarks Thursday All events are held in Room 104, except for the poster session, which takes place in the Pacific Ballroom 10:00 – 14:00 Registration Desk Opens 12:00 – 12:45 Lunch 12:45 – 13:05 Opening Remarks – Raia Hadsell , DeepMind 13:05 – 13:40 Invited Talk – Joelle Pineau Head Facebook AI Research (Montreal Lab)/ Mc Gill. Improving health-care: challenges and opportunities for reinforcement learning 13:40 – 13:55 Contributed Talk: Zhenyi Tang, University of Illinois. Harnessing Adversarial Attacks on Deep Reinforcement Learning for Improving Robustness. 13:55 – 14:10 Contributed Talk: Hoda Heidari, ETH Zurich. A General Framework for Evaluating Callout Mechanisms in Repeated Auctions. 14:10 – 14:20 Coffee Break 14:20 – 15:20 Research and Career Advice Roundtable 15:20 – 15:55 Invited Talk – Nina Mishra Amazon. Time-Critical Machine Learning 15:55 – 16:15 Contributed Talk: Sarah Bouchat, Nothwestern University. Engaging Experts: A Dirichlet Process Approach to Divergent Elicited Priors in Social Science. 16:15 – 16:35 Contributed Talk: Nesreen K Ahmed, Intel Labs. Representation Learning in Large Attributed Graphs. 16:35 – 16:40 Closing Remarks 16:40 – 18:05 Poster Session (Coffee and Snacks Served) Monday, Dec 4, 12:20 pm to 2:00 pm and Thursday Dec 7, 4:35pm -6:00pm, open to WiML and NIPS attendees 350+ posters covering theory, methodology, and applications of machine learning will be presented across 2 poster sessions. The list of posters and authors can be found in the program book Accepted posters (with abstracts) . Abstracts listed here are for archival purposes and do not constitute proceedings for this workshop. Poster size: Up to 48 inches tall and 60 inches wide. We recommend printing in the stand A0 size (33.11 inches by 46.81 inches). You can orient the poster in portrait or landscape as long as it fits within the specified dimensions. This year we have four categories of mentorship roundtables: Research Roundtables (Tables 1-22), Career Advice Roundtables (Tables 23-42), NIPS Paper Discussion (Tables 43-50), Company Career Tables (Tables 51-63). On Monday 4th December, these tables will take place at 2:50pm – 3:50pm Table 1: Reinforcement learning I – Katja Hofmann, Microsoft Research Table 2: Reinforcement learning II – Oriol Vinyals, DeepMind Table 3: Deep learning I – Yoshua Bengio, MILA – Université de Montréal Table 4: Deep learning II – Doina Precup, McGill University / Head DeepMind Montreal Table 5: Bayesian methods I – Meire Fortunato, DeepMind Table 6: Bayesian methods II –Neil Lawrence, Amazon Research Cambridge Table 7: Graphical models – Anima Anandkumar, Amazon Web Services/ Caltech Table 8: Statistical inference, estimation and Optimization – Irina Kukuyeva, Dia&Co Table 9: Neuroscience – Katharina Volz, Founder OccamzRazor Table 10: Robotics I – Bonolo Mathibela, IBM Research Table 11: Black Box vs Open Box ML Approaches – Barbara Engelhardt, Assistant Professor, Princeton Table 12: Natural language processing – George Dahl, Google Brain Table 13: Biological Applications – Luisa Cutillo, University Parthenope of Naples Table 14: Healthcare/Clinical Applications – Marzyeh Ghassemi, MIT/Verily Table 15: Causal Inference and Counterfactuals – Sara Magliacane, IBM Research Table 16: Computer Vision – Amy Zhang, Facebook AI Research Table 17: Fairness, accountability, transparency in ML – Christian Borgs, Microsoft Research Table 18: Social Sciences Application – Timnit Gebru, Microsoft Research Table 19: Music Applications – Vidhya Murali, Spotify USA Inc Table 20: Business Applications – Pallika Kanani, Oracle Labs Table 21: Industrial Applications in AI/ Commercialising your Research – Jennifer Schumacher, 3M Table 22: Technical AGI Safety – Victoria Krakovna, DeepMind Table 23: Creative AI Applications (Art, Music, Design) – Luba Elliott, iambic.a Table 24: Work-Life Balance (Industry) – Hanna Wallach, Microsoft Table 25: Work-Life Balance (Academia) – Joelle Pineau, Head Facebook AI Research Montreal/ Professor McGill University Table 26: Life with Kids / Work-life balance – Caitlin Smallwood Table 27: Getting a Job (Industry) – Beth Zeranski, Microsoft Table 28: Getting a Job (Academic) – Yisong Yue, Caltech/ Tamara Broderick MIT Table 29: Doing a Postdoc – Adriana Romero, Facebook AI Research Table 30: Choosing between Academia and Industry – Daniel Jiang, University of Pittsburgh Table 31: Choosing between Academia and Industry – Samy Bengio, Google Brain Table 32: Doing Research in Industry – Natalia Neverova, Facebook AI Research; Stacey Svetlichnaya, Flickr / Yahoo Research Table 33: Keeping up with academia while in industry – Nevena Lazic, Google Table 34: Surviving Graduate School – Lily Hu, Salesforce Research; Table 35: Establishing Collaborators – Moustapha Cisse, Facebook AI Research Table 36: Scientific Communication – Chris Bishop, Lab Director Microsoft Research Cambridge Table 37: Building your Professional Brand – Katherine Gorman, Talking Machines/Collective Next Table 38: Founding Startups/ Building your Professional Brand – Rachel Thomas, Fast.AI/ University of San Francisco Table 39: Founding Startups – Philippe Beaudoin, Element AI Table 40: Early-Stage Start-ups using Machine Learning/Deep Learning – Lisha Li, Amplify Partners Table 41: Joining Start-ups – Lavanya Tekumalla, Amazon Table 42: Finding Mentors/ Networking – Lisa Amini, Director IBM Research AI Table 43: Long-term Career Planning – Jennifer Chayes, Managing Director, Microsoft Research NE & NYC Table 44: NIPS Paper Discussion: SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement – Maithra Raghu, Google Brain and Cornell University Table 45: NIPS Paper Discussion: Self-supervised Learning of Motion Capture – Katerina Fragkiadaki, Carnegie Mellon University Table 46: NIPS Paper Discussion: Linear regression without correspondence – Daniel Hsu, Columbia University Table 47: NIPS Paper Discussion: A-NICE-MC: Adversarial Training for MCMC – Jiaming Song, Stanford University Table 48: NIPS Paper Discussion: Learning multiple visual domains with residual adapters – Sylvestre-Alvise Rebuffi, University of Oxford Table 49: NIPS Paper Discussion: Efficient Use of Limited-Memory Resources to Accelerate Linear Learning – Celestine Dünner, IBM Research Table 50: NIPS Paper Discussion: Variational Inference via χ Upper Bound Minimization – Adji Bousso Dieng, Columbia University Table 51: NIPS Paper Discussion: Robust Hypothesis Test for Functional Effect with Gaussian Processes – Jeremiah Liu, Harvard University Table 52: NIPS Paper Discussion: Bayesian Dyadic Trees and Histograms for Regression – Stéphanie van der Pas, Leiden University Table 52: Careers@ElementAI Table 53: Careers@Facebook Table 54: Careers@DeepMind Table 55: Careers@Capital One Table 56: Careers@Criteo Table 57: Careers@Microsoft Table 58: Careers@Intel Table 59: Careers@Google Table 60: Careers@Airbnb Table 61: Careers@Apple Table 62: Careers@IBM Table 63: Careers@NVIDIA Table 64: Careers@Pandora This year we have four categories of mentorship roundtables: Research Roundtables (Tables 1-25), Career Advice Roundtables (Tables 26-43), NIPS Paper Discussion (Tables 44-57), Company Career Tables (Tables 51-63). On Thursday 7th December, these tables will take place at 2:20pm – 3:20pm Table 1: Reinforcement learning I – Raia Hadsell, DeepMind Table 2: Black Box vs Open Box ML Approaches – Barbara Engelhardt, Assistant Professor, Princeton Table 3: Deep learning I – Anima Anandkumar, Amazon Web Services/ Caltech Table 4: Deep learning II – Amy Zhang, Facebook AI Research Table 5: Bayesian methods I – Chris Bishop, Lab Director Microsoft Research Cambridge Table 6: Bayesian methods II – Zoubin Ghahramani, University of Cambridge Table 7: Graphical models – David Blei, Columbia University Table 8: Generative Models – Ian Goodfellow, Google Brain Table 9: Technical AGI Safety – Shane Legg, Founder DeepMind Table 10: Kernel Methods – Corinna Cortes, Head of Google Research Table 11: Neuroscience – Katharina Volz, Founder OccamzRazor Table 12: Robotics – Table 13: Natural language processing – George Dahl, Google Brain Table 14: Statistical inference and estimation – Timnit Gebru, Microsoft Research Table 15: Biological Applications – Luisa Cutillo, University Parthenope of Naples Table 16: Healthcare/Clinical Applications – Marzyeh Ghassemi, MIT/Verily Table 17: Optimization – Irina Kukuyeva, Dia & Co Table 18: Causal Inference and Counterfactuals – Sara Magliacane, IBM Research Table 19: Computer Vision – Natalia Neverova, Facebook AI Research; Table 20: Fairness, accountability, transparency in ML I – Christian Borgs, Microsoft Research Table 21: Fairness, accountability, transparency in ML II – Nyalleng Moorosi, Council for Scientific and Industrial Research Table 22: Learning Theory – Hoda Heidari, ETHZ Table 23: Social Sciences Application – Lise Getoor, UC Santa Cruz Table 24: Business Applications – Pallika Kanani, Oracle Labs Table 25: Industrial Applications in AI/ Commercialising your Research – Jennifer Schumacher, 3M Table 26: Work-Life Balance (Industry) – Amy Nicholson, Olivia Klose, Microsoft Table 27: Work-Life Balance (Academia) – Neil Lawrence, Amazon Research Cambridge Table 28: Life with Kids / Work-life balance – Caitlin Smallwood, Netflix Table 29: Getting a Job (Industry) – Aleatha Parker-Wood Symantec Table 30: Getting a Job (Academic) – Yisong Yue, Caltech Table 31: Doing a Postdoc – Aida Nematzadeh, UC Berkeley Table 32: Choosing between Academia and Industry – Daniel Hsu, Columbia University Table 33: Choosing between Academia and Industry – Adriana Romero, Facebook AI Research Table 34: Doing Research in Industry – Stacey Svetlichnaya Flickr / Yahoo Research Table 35: Keeping up with academia while in industry – Chew-Yean Yam, Microsoft Table 36: Surviving Graduate School – Shruthi Kubatur, Nikon Research Corporation of America Table 37: Establishing Collaborators/ Long-term Career Planning – Jennifer Chayes, Managing Director, Microsoft Research NE & NYC Table 38: Scientific Communication – Katherine Gorman, Talking Machines/ Collective Next Table 39: Building your Professional Brand/ Founding Startups – Rachel Thomas, Fast.AI/ University of San Francisco Table 40: Founding Startups – Philippe Beaudoin, Element AI Table 41: Early-Stage Start-ups using Machine Learning/Deep Learning – Lisha Li, Amplify Partners Table 42: Joining Start-ups – Lavanya Tekumalla, Amazon Table 43: Networking/ Finding Mentors – Muhammad Jamal Afridi, 3M Table 44: Table 45: NIPS Paper: A-NICE-MC: Adversarial Training for MCMC – Jiaming Song, Stanford University Table 46: NIPS Paper: Learning multiple visual domains with residual adapters – Sylvestre-Alvise Rebuffi, University of Oxford Table 47: NIPS Paper: Variational Inference via χ Upper Bound Minimization – Adji Bousso Dieng, Columbia University Table 48: NIPS Paper: A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control – Fanny Yang, UC Berkley Table 49: NIPS Paper: Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin – Ritambhara Singh, University of Virginia Table 50: NIPS Paper: Style Transfer from Non-parallel Text by Cross-Alignment – Tianxiao Shen, MIT CSAIL Table 51: NIPS Paper: Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model – Devi Parikh, Georgia Tech / Facebook AI Research Table 52: Table 53: NIPS Paper: Inferring Generative Model Structure with Static Analysis – Paroma Varma, Stanford University Table 54: NIPS Paper: Concrete Dropout (Topic: Bayesian Deep Learning) – Yarin Gal, University of Oxford Table 55: NIPS Paper: Do Deep Neural Networks Suffer from Crowding? – Anna Volokitin, ETH Zurich Table 56 Table 57: NIPS Paper: Deanonymization in the Bitcoin P2P Network – Giulia Fanti, Carnegie Mellon University Table 58: Careers@ElementAI Table 59: Careers@Facebook Table 60: Careers@DeepMind Table 61: Careers@Capital One Table 62: Careers@Criteo Table 63: Careers@Microsoft Table 64: Careers@Intel Table 65: Careers@Google Table 66: Careers@Airbnb Table 67: Careers@Apple Table 68: Careers@IBM Table 69: Careers@NVIDIA Table 70: Careers@Pandora Call for Participation The 12th WiML Workshop is co-located with NIPS in Long Beach, California on Monday, December 4th and Thursday, December 7th, 2017. The workshop is a two-day event with invited speakers, oral presentations, and posters. The event brings together faculty, graduate students, and research scientists for an opportunity to connect and exchange ideas. There will be a panel discussion and a mentoring session to discuss current research trends and career choices in machine learning. Underrepresented minorities and undergraduates interested in pursuing machine learning research are encouraged to participate. While all presenters will identify primarily as female, all genders are invited to attend. This is a technical workshop with exciting technical talks. Important Dates September 12th, 2017 11:59 pm PST – Extended Abstract submission deadline September 8th, 2017 11:59 pm PST – Abstract submission deadline October 16th, 2017 – Notification of abstract acceptance November 1st, 2017 – Travel grant application deadline November 14th, 2017 – Registration Deadline December 4th, 2017 – Workshop Day 1 December 7th, 2017 – Workshop Day 2 Submission Instructions We strongly encourage primarily female-identifying students, post-docs and researchers in all areas of machine learning to submit an abstract describing new, previously, or concurrently published research. We welcome abstract submissions, in theory, methodology, as well as applications. Abstracts may describe completed research or work-in-progress. While the presenting author need not be the first author of the work, we encourage authors to highlight the contribution of female authors — particularly the presenting author — in the abstract. Authors of accepted abstracts will be asked to present their work in a poster session. A few authors will be selected to give 15 minute oral presentations. Submissions will be peer-reviewed in a double-blind setting. Authors are encouraged to sign up to review for WiML, with a sign-up option available upon submission. Student and post-doc authors who review for WiML will be eligible for travel awards. Submission page: https://cmt3.research.microsoft.com/WiML2017 Style guidelines: Abstracts must not include identifying information Abstracts must be no more than 1 page (including any references, tables, and figures) submitted as a PDF. Main body text must be 11 points in size. Do not include any supplementary files with your submission. Content guidelines: Your abstract should stand alone, without linking to a longer paper or supplement. You should convey motivation and give some technical details of the approach used. While we appreciate that space is limited, some experimental results are likely to improve reviewers’ opinions of your paper. Acceptance criteria: All accepted papers must be presented by a primarily female-identifying author. Abstracts will be reviewed by at least two reviewers plus an area chair, who will use the following criteria: Is this paper appropriate for WiML? I.e. Does it describe original research in Machine Learning or related fields? Does the abstract describe work that is novel and/or an interesting application? Does the abstract adequately convey the material that will be presented? Examples of accepted abstracts from previous years. Due to the volume of submissions anticipated, we are unable to review any submitted materials besides the requested abstract. Travel Scholarships Registration is free. Travel Awards are available for presenting authors only. To qualify, the author must be a student or post-doc, their abstract must be accepted, and they must volunteer to serve as a reviewer for WiML. The amount of the travel award varies by the author’s geographical location and the total amount of funding WiML receives from our sponsors. In the past awards ranging from $300-$900 have been granted. Organizers Negar Rostamzadeh (Element AI) Ehi Nosakhare (MIT) Danielle Belgrave (Imperial College London) Genna Gliner (Princeton University) Maja Rudolph (Columbia University) PLATINUM SPONSORS GOLD SPONSORS SILVER SPONSORS BRONZE SPONSORS SUPPORTER Committee ORGANIZERS Genna Gliner PhD student at the University of Princeton Ehimwenma Nosakhare Phd student at MIT EECS Maja Rudolph PhD student at Columbia University Danielle Belgrave Research Fellow at Imperial College of London Negar Rostamzadeh Research Scientist at Element AI FAQs Do you have a list of members? How can I join WiML? WiML doesn’t have “members” per se, any women working in machine learning can be part of the WiML network. We have a mailing list for anyone to post announcements of interest to the WiML network and an opt-in, necessarily incomplete directory of women working in machine learning . How can I join the WiML mailing list? Join the mailing list directly here . What kind of events do you organize? Our flagship event is the annual WiML Workshop, typically co-located with NeurIPS, a machine learning conference. We also organize an “un-workshop” at ICML, as well as small events (e.g. lunches and receptions) at other machine learning conferences, such as CoRL, COLT, etc. Check out our events page for up-to-date listings of events. Do you have local meetups? No, but check out WiMLDS (website, Twitter), another organization that supports women in machine learning by organizing local meetups. How do I reach the WiML network? Use our mailing list . How can I sponsor WiML? Thank you for your interest in sponsoring WiML! See this page for more information. I am looking for an invited speaker / panelist / area chair / program committee member etc. Can WiML help me? Use our directory of women in machine learning or post this opportunity to our mailing list . I want to circulate a job posting. Can WiML help me? Post directly to our mailing list . How can I support WiML? You can: Post interesting opportunities and job postings to our mailing list . Use our directory of women in machine learning to find invited speakers, panelists, area chairs, program committee members, etc, or post these opportunities to our mailing list . Sponsor us. See this page for more information. Volunteer at one of our events. Check out our events page for up-to-date listings of events. Apply to be an area chair or reviewer at WiML Workshop (see this year’s workshop website for info). Take pictures at our events and share with us (tag @wimlworkshop on Twitter). If you see us mentioned in the media, send us a link at info@wimlworkshop.org . And many others! How did WiML start? What's the founding story? Hanna Wallach, Jennifer Wortman Vaughan, Lisa Wainer, and Angela Yu shared a room at NIPS 2005. Late one night, they talked about how exciting it was that there were FOUR female students at NIPS that year. They tried to list all the women in machine learning they know of and got to 10, then started talking about creating a meeting or gathering for all these women and perhaps others that they didn’t know about. Jenn, Lisa, and Hanna put together a proposal for a session at the 2006 Grace Hopper Celebration of Women in Computing that would feature talks and posters by female researchers and students in machine learning. The 1st WiML workshop was co-located with the 2006 Grace Hopper Celeberation. In 2008, WiML Workshop moved to NIPS (renamed NeurIPS in 2018) and there has been a WiML Workshop at NeurIPS every year since. In 2020, WiML introduced an “un-workshop” at ICML based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. Read more WiML history here ! I am a man. Can I attend WiML? Yes. Allies are welcome to attend! Note, however, that all speakers and poster presenters will primarily identify as women, nonbinary, or gender-nonconforming, as our goal is to promote them and their work within the machine learning community. What are the mentorship roundtables? Each table seats 8-10 people (including mentors), with two mentors leading the discussion on a particular topic at each table. WiML attendees rotate between tables every 15-20 minutes. This allows attendees to gain exposure to different topics, and mentors to meet a large number of WiML attendees. Is WiML an archival venue? No, WiML is a non-archival venue. This means that, if your contribution is accepted, we will not be asking you to submit a camera-ready version of it, nor will we publish it anywhere (neither online nor in proceedings of any sort). We will only make the title and authors’ names available in the program book. I have a question that isn't answered here. How do I reach you? We receive a lot of email. Help us help you by reaching out through the appropriate channels. Job posting, announcement, CFP, etc: Post directly to WiML mailing list . Have event pictures to share: post on Twitter and tag @wimlworkshop Workshop enquiries: workshop@wimlworkshop.org If you are a company interested in sponsoring WiML: sponsorship@wimlworkshop.org Any other enquiries: info@wimlworkshop.org If you email us, don’t cc multiple email addresses — this saves us time routing your email to one mailbox, and reduces the chances of your email getting lost. Thank you in advance! Back To Top

13th Women in Machine Learning Workshop (WiML 2018) The 13th WiML Workshop is co-located with NeurIPS in Montreal, Quebec on Monday, December 3rd, 2018. Speakers Logistics Program Call for Participation Committee FAQ Code Of Conduct Machine learning is one of the fastest growing areas of computer science research. Search engines, text mining, social media analytics, face recognition, DNA sequence analysis, speech and handwriting recognition, healthcare analytics are just some of the applications in which machine learning is routinely used. In spite of the wide reach of machine learning and the variety of theory and applications, it covers, the percentage of female researchers is lower than in many other areas of computer science. Most women working in machine learning rarely get the chance to interact with other female researchers, making it easy to feel isolated and hard to find role models. The annual Women in Machine Learning Workshop is the flagship event of Women in Machine Learning . This technical workshop gives female faculty, research scientists, and graduate students in the machine learning community an opportunity to meet, network and exchange ideas, participate in career-focused panel discussions with senior women in industry and academia and learn from each other. Underrepresented minorities and undergraduates interested in machine learning research are encouraged to attend. We welcome all genders; however, any formal presentations, i.e. talks and posters, are given by women. We strive to create an atmosphere in which participants feel comfortable to engage in technical and career-related conversations. Now in its 13th year, the 2018 workshop is co-located with NIPS in Montreal, Canada. A History of WiML poster was created to celebrate the 10th workshop, also held in 2015 in Montreal, Canada. Besides this un-workshop and annual workshop which is co-located with NeurIPS, Women in Machine Learning also organizes events such as lunch at ICML and AAAI conferences, maintains a public directory of women active in ML, profiles the research of women in ML, and maintains a list of resources for women working in ML. Invited Speakers Emma Brunskill Stanford Po-Ling Loh UW-Madison Raquel Urtasun Uber / University of Toronto Isabel Kloumann Facebook Megan Maher Apple Cascaded Dataset QA Lanlan Liu University of Michigan Jennifer Drexler MIT Amanda Rios USC Katherine M. Kinnaird Smith College Location This workshop takes place in Palais des Congrès in Montreal. Convention Center Rooms More details about the workshop and poster sessions will be provided shortly. PROGRAM MENTORSHIP ROUNDTABLES SPONSOR TABLES Sunday, December 2 12:00 pm – 2:00 pm Registration desk open 6:00 pm – 10:00 pm WiML Dinner (Optional) (Separate registration required) Monday, December 3 All events are held in Rooms 517AB and 516C, except for the evening poster session, which takes place in Room 210. 8:00 am – 12:00 pm Registration Open 8:00 am - 9:00 am Breakfast 9:00 am – 9 :10 am Opening Remarks – (WiML Organizers) 9:10 am – 9:50 am Invited talk 1 – Isabel Kloumann 9:50 am – 10:10 am Contributed talk 1 – Lanlan Liu 10:10 am – 10:30 am Contributed talk 2 – Megan Maher 10:30 am – 10:50 am Coffee Break 10:50 am – 11:30 am Invited talk 2 – Po-Ling Loh 11:30 am – 11:50 am Contributed talk 3 – Amanda Rios 11:50 am – 1:00 pm Mentorship Circles 11:00 pm – 2:30 pm Lunch + Poster Session 2:30 pm – 3:10 pm Invited talk 3 – Raquel Urtasun 3:10 pm – 3:30 pm Contributed talk 4 – Jennifer Drexler 3:30 pm – 3:50 pm Coffee Break 3:50 pm – 4:30 pm Invited talk 4 – Emma Brunskill 4:30 pm – 4:40 pm Closing remarks 6:00 pm – 7:30 pm Poster Session 2 (co-located with NeurIPS reception) NeurIPS Main Conference (NeurIPS registration required) 5:00 pm NeurIPS Opening Remarks This year we have four categories of mentorship roundtables: Research Roundtables (Tables 1-15), Career Advice Roundtables (Tables 17-44), Company Career Tables (Tables 45-61). Monday, December 3rd: 11:50am - 1:00pm Tables subject to change Research topics Table 1: Reinforcement learning – Anima Anandkumar NVIDIA/Caltech Professor (post-tenure) Table 2: Bayesian optimization and causal inference – Eytan Bakshy Facebook Research Scientist/ Engineer Table 3: Balance: between academia and industry, work and life – Emily Fox University of Washington Professor (post-tenure) Table 4: Deep learning – Yarin Gal University of Oxford Professor (post-tenure) Table 5: Bayesian models, graphical models, learning theory and statistical inference – Po-Ling Loh UW-Madison Professor (pre-tenure) Table 6: Systems for ML – Kim Hazelwood Facebook Engineering Manager (former tenured Professor) Table 7: Causal inference and counterfactuals – Sara Magliacane IBM Research Researcher Table 8: Computer Vision – Adriana Romero Facebook AI Research Research Scientist and adjunct professor Table 9: Time series – Negar Ghourchian Aerial Technologies Director of AI Table 10: Robotics – Sanja Fidler University of Toronto, NVIDIA Professor (pre-tenure) Table 11: Healthcare applications – Tess Berthier Imagia Research Scientist/ Engineer Table 12: Fairness – Joaquin Quiñonero Candela Facebook Director of AI Engineering Table 13: Natural Language Processing – Aida Nematzadeh DeepMind Research Scientist/ Engineer Table 14: Social science – Svitlana Volkova Pacific Northwest National Laboratory Research Scientist/ Engineer Table 15: Recommender system, information retrieval – Putra Manggala Shopify Data Scientist/ Engineer Table 16: Data Visualization – Fernanda Viegas Google Research Scientist/ Engineer Career and general advice topics Table 17: Work life balance (industry) I – Dilan Gorur DeepMind Research Scientist/ Engineer Table 18: Work life balance (industry) II – Yinyin Liu Intel AI Head of Data Science, Intel AI Table 19: Work life balance (academia) – Isabel Valera Max Planck Institute for Intelligent Systems Group leader Table 20: Life with kids – Corinna Cortes Google Research Scientist/ Engineer Table 21: Getting a job (industry) I – Been Kim Google brain Research Scientist/ Engineer Table 22: Getting a job (industry) II – Lily Hu Salesforce Research Research Scientist/ Engineer Table 23: Getting a job (academia) – Sinead Williamson UT Austin / Amazon Professor (pre-tenure);Research Scientist/ Engineer Table 24: Doing a Post Doc – Timnit Gebru Google Research Scientist/ Engineer Table 25: Academia vs. Industry I – Claire Vernade Google DeepMind Research Scientist/ Engineer Table 26: Academia vs. Industry II – Raquel Urtasun Uber ATG / University of Toronto Chief Scientist, Associate Professor Table 27: Research in Industry I – Joelle Pineau McGill University / Facebook Professor (post-tenure), Research Scientist/ Engineer Table 28: Research in Industry II – Lisa Amini IBM Research AI Research Scientist/ Engineer Table 29: Keeping up with academia while in industry I Ian Goodfellow Google AI Research Scientist/ Engineer Table 30: Keeping up with academia while in industry II David Vazquez Element AI Research Scientist/ Engineer Table 31: Surviving graduate school I – Chelsea Finn Google, UC Berkeley Postdoc;Professor (pre-tenure);Research Scientist/ Engineer Table 32: Surviving graduate school II – Priya Donti Carnegie Mellon University PhD student Table 33: Seeking funding: fellowships and grants – Sarah Tan Cornell / UCSF PhD student Table 34: Establishing collaborations – Eric Sodomka Facebook Research Scientist/ Engineer;Data Scientist/ Engineer Table 35: Joining startups – Rachel Thomas fast.ai Research Scientist/Engineer;co-founder Table 36: Career advice & Work/life balance – Neil Lawrence Amazon, University of Sheffield Machine Learning Director, Professor Table 37: Founding startups – Sarah Osentoski Free Agent Sole Proprietor Table 38: Scientific communication – Katie Kinnaird Brown University Postdoc Table 39: Networking – Inmar Givoni Uber ATG Sr Engineering Manager Table 40: Building your professional brand – Hanna Wallach Microsoft Professor (post-tenure);Research Scientist Table 41: Long-term career planning – Negar Rostamzadeh Element AI Research Scientist/ Engineer Table 42: Commercializing your research – Nesreen Ahmed Intel Research Senior Research Scientist Table 43: Finding Mentors – Feryal Behbahani Latent Logic Research Scientist/ Engineer Table 44: Junior faculty life – Emma Brunskill Stanford Assistant Professor Industry career tables Table 45: Careers @ DeepMind Doina Precup, Anna Harutyunyan, Daniel Toyama Table 46: Careers @ Facebook Amy Zhang Table 47: Careers @ Google Kristen Hofstetter Table 48: Careers @ IBM Lisa Amini Table 49: Careers @ CapitalOne Hongjun Wang Table 50: Careers @ Adobe Dhanashree Balaram Table 51: Careers @ Amazon Dilek Hakkani-Tur, Hongyi Liu, Cheng Tang Table 52: Careers @ Apple Michelle Chen Table 53: Careers @ Dessa Jodie Zhu Table 54: Careers @ Intel Jennifer Healey, Anna Bethke Table 55: Careers @ Microsoft Wendy Tay Table 56: Careers @ Samsung Daedeepya Yendluri, Ghazaleh Moradiannejad Table 57: Careers @ Unity Marilyn Hommes Table 58: Careers @ Element AI Perouz Taslakian Table 59: Careers @ Oracle Labs John Tristan Table 60: Careers @ Shell Neilkunal Panchal, Jeremy Vila, Mauricio Araya, Rayetta Seals Table 61: Careers @ Wayfair Patricia Stichnoth Recruitment Tables Recruitment tables from our major sponsors will be set up in room 516c for the duration of the workshop. Table A: Careers @ IBM Table B: Careers @ Apple Table C: Careers @ Samsung Table D: Careers @ Google Table E: Careers @ Unity3D Table F: Careers @ Amazon Table G: Careers @ Facebook Table H: Careers @ Adobe Table I: Careers @ Microsoft Table J: Careers @ Deepmind Table K: Careers @ Dessa Table L: Careers @ Intel Call for Participation The 13th WiML Workshop is co-located with NIPS in Montreal, Quebec on Monday, December 3rd, 2018. The workshop is a one-day event with invited speakers, oral presentations, and posters. The event brings together faculty, graduate students, and research scientists for an opportunity to connect and exchange ideas. There will be a panel discussion and a mentoring session to discuss current research trends and career choices in machine learning. Underrepresented minorities and undergraduates interested in pursuing machine learning research are encouraged to participate. While all presenters will identify primarily as female, all genders are invited to attend. Important Dates September 7th, 2018 11:59pm PST – Abstract submission deadline October 15th, 2018 – Notification of abstract acceptance TBA – Travel grant application deadline TBA – Registration Deadline December 3rd, 2018 – Workshop Day Submission Instructions We strongly encourage students, post-docs and researchers who primarily identify as women or nonbinary in all areas of machine learning to submit an abstract describing new, previously, or concurrently published research. We welcome abstract submissions, in theory, methodology, as well as applications. Abstracts may describe completed research or work-in-progress. While the presenting author need not be the first author of the work, we encourage authors to highlight the contribution of women — particularly the presenting author — in the abstract. Authors of accepted abstracts will be asked to present their work in a poster session. A few authors will be selected to give 15 minute oral presentations. Submissions will be peer-reviewed in a double-blind setting. Authors will be automatically added to the reviewer pool and asked to review. Student and post-doc authors who review for WiML will be eligible for travel awards. Submission page: WiML 2018 CMT Style guidelines: Abstracts must not include identifying information Abstracts must be no more than 1 page (including any references, tables, and figures) submitted as a PDF in NIPS format. Upload the PDF, do not paste in the abstract box. Do not include any supplementary files with your submission. Content guidelines: Your abstract should stand alone, without linking to a longer paper or supplement. You should convey motivation and give some technical details of the approach used. While we appreciate that space is limited, some experimental results are likely to improve reviewers’ opinions of your paper. Acceptance criteria: All accepted papers must be presented by the submitting author, or another author who identifies primarily as a woman or nonbinary. Abstracts will be reviewed by at least two reviewers plus an area chair, who will use the following criteria: Is this paper appropriate for WiML? I.e. Does it describe original research in Machine Learning or related fields? Does the abstract describe work that is novel and/or an interesting application? Does the abstract adequately convey the material that will be presented? Examples of accepted abstracts from previous years. Due to the volume of submissions anticipated, we are unable to review any submitted materials besides the requested abstract. Travel Scholarships Travel Awards are available for presenting authors only. To qualify, the author must be a student or postdoc, their abstract must be accepted, and they must volunteer to serve as a reviewer for WiML. The amount of the travel award varies by the author’s geographical location and the total amount of funding WiML receives from our sponsors. In the past awards ranging from $300-$900 have been granted. All travel grants are administered as refunds and no funding is allocated before the conference. Area Chairs If you are interested in being an area chair, please email wiml2018@wimlworkshop.org with subject line “Area Chair for WiML 2018”. The role of area chairs is to evaluate the reviews, write a final meta-review and suggest acceptance/reject decisions for each abstract. We expect each area chair to be responsible for 10 short abstracts with each abstract having a maximum word limit of 500 words. Organizers Audrey Durand (McGill University) Aude Hofleitner (Facebook) Nyalleng Moorosi (CSIR) Sarah Poole (Stanford University) Amy Zhang (McGill University / Facebook AI Research) PLATINUM SPONSORS DIAMOND SPONSORS GOLD SPONSORS SILVER SPONSORS BRONZE SPONSORS SUPPORTERS BRONZE SPONSORS Committee ORGANIZERS Audrey Durand Mila / McGill University Aude Hofleitner Facebook Nyalleng Moorosi Google AI Sarah Poole Verily Amy Zhang Mila / McGill University / Facebook BOARD OF DIRECTORS Katherine M. Kinnaird (President) Smith College Allison Chaney (Vice President) Princeton University Jennifer Healey (Vice President) Intel Labs Jessica Thompson (Secretary) Université de Montréal Sarah Brown (Treasurer) Brown University Tamara Broderick Massachusetts Institute of Technology Raia Hadsell DeepMind Abigail Jacobs University of California, Berkeley Been Kim Google Brain Katie Niehaus Freenome Sarah Tan Cornell University / UCSF SENIOR ADVISORY COUNCIL Hanna Wallach (WiML Co-Founder) Microsoft Research / UMass Amherst Jenn Wortman Vaughan (WiML Co-Founder) Microsoft Research Emma Brunskill Stanford University Finale Doshi-Velez Harvard University Barbara Engelhardt Princeton University Marzyeh Ghassemi University of Toronto / Vector Institute Inmar Givoni Uber ATG Katherine Heller Duke University Pallika Kanani Oracle Labs Claire Monteleoni University of Colorado Boulder Sarah Osentoski Mayfield Robotics Svitlana Volkova Pacific Northwest National Laboratory Sinead Williamson University of Texas at Austin Alice Zheng Amazon FAQs Do you have a list of members? How can I join WiML? WiML doesn’t have “members” per se, any women working in machine learning can be part of the WiML network. We have a mailing list for anyone to post announcements of interest to the WiML network and an opt-in, necessarily incomplete directory of women working in machine learning . How can I join the WiML mailing list? Join the mailing list directly here . What kind of events do you organize? Our flagship event is the annual WiML Workshop, typically co-located with NeurIPS, a machine learning conference. We also organize an “un-workshop” at ICML, as well as small events (e.g. lunches and receptions) at other machine learning conferences, such as CoRL, COLT, etc. Check out our events page for up-to-date listings of events. Do you have local meetups? No, but check out WiMLDS (website, Twitter), another organization that supports women in machine learning by organizing local meetups. How do I reach the WiML network? Use our mailing list . How can I sponsor WiML? Thank you for your interest in sponsoring WiML! See this page for more information. I am looking for an invited speaker / panelist / area chair / program committee member etc. Can WiML help me? Use our directory of women in machine learning or post this opportunity to our mailing list . I want to circulate a job posting. Can WiML help me? Post directly to our mailing list . How can I support WiML? You can: Post interesting opportunities and job postings to our mailing list . Use our directory of women in machine learning to find invited speakers, panelists, area chairs, program committee members, etc, or post these opportunities to our mailing list . Sponsor us. See this page for more information. Volunteer at one of our events. Check out our events page for up-to-date listings of events. Apply to be an area chair or reviewer at WiML Workshop (see this year’s workshop website for info). Take pictures at our events and share with us (tag @wimlworkshop on Twitter). If you see us mentioned in the media, send us a link at info@wimlworkshop.org . And many others! How did WiML start? What's the founding story? Hanna Wallach, Jennifer Wortman Vaughan, Lisa Wainer, and Angela Yu shared a room at NIPS 2005. Late one night, they talked about how exciting it was that there were FOUR female students at NIPS that year. They tried to list all the women in machine learning they know of and got to 10, then started talking about creating a meeting or gathering for all these women and perhaps others that they didn’t know about. Jenn, Lisa, and Hanna put together a proposal for a session at the 2006 Grace Hopper Celebration of Women in Computing that would feature talks and posters by female researchers and students in machine learning. The 1st WiML workshop was co-located with the 2006 Grace Hopper Celeberation. In 2008, WiML Workshop moved to NIPS (renamed NeurIPS in 2018) and there has been a WiML Workshop at NeurIPS every year since. In 2020, WiML introduced an “un-workshop” at ICML based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. Read more WiML history here ! I am a man. Can I attend WiML? Yes. Allies are welcome to attend! Note, however, that all speakers and poster presenters will primarily identify as women, nonbinary, or gender-nonconforming, as our goal is to promote them and their work within the machine learning community. What are the mentorship roundtables? Each table seats 8-10 people (including mentors), with two mentors leading the discussion on a particular topic at each table. WiML attendees rotate between tables every 15-20 minutes. This allows attendees to gain exposure to different topics, and mentors to meet a large number of WiML attendees. Is WiML an archival venue? No, WiML is a non-archival venue. This means that, if your contribution is accepted, we will not be asking you to submit a camera-ready version of it, nor will we publish it anywhere (neither online nor in proceedings of any sort). We will only make the title and authors’ names available in the program book. I have a question that isn't answered here. How do I reach you? We receive a lot of email. Help us help you by reaching out through the appropriate channels. Job posting, announcement, CFP, etc: Post directly to WiML mailing list . Have event pictures to share: post on Twitter and tag @wimlworkshop Workshop enquiries: workshop@wimlworkshop.org If you are a company interested in sponsoring WiML: sponsorship@wimlworkshop.org Any other enquiries: info@wimlworkshop.org If you email us, don’t cc multiple email addresses — this saves us time routing your email to one mailbox, and reduces the chances of your email getting lost. Thank you in advance! Back To Top

WiML Code of Conduct The open exchange of ideas, the freedom of thought and expression, and respectful scientific debate are central to the goals of Women in Machine Learning, Inc. (“WiML”) activities; this requires a community and an environment that recognizes and respects the inherent worth of every person. The purpose of this Code of Conduct (CoC) is to outline expected standards of behaviour during WiML activities. Scope This CoC applies to all WiML activities, including but not limited to: Events organized, hosted, co-branded, or in cooperation with WiML Submissions and reviewing processes run by WiML. Communications sent through communication channels associated with WiML, including but not limited to social media. Meetings and discussions associated with WiML activities. If an activity is in cooperation with another organization, if the other organization has its own CoC, the union of both CoCs apply. Responsibility All attendees, speakers, mentors, panelists, area chairs, reviewers, sponsors, contractors, organizers, volunteers, members of the WiML Board of Directors and Senior Advisory Council (referred to as “Participants” collectively throughout this document) involved in WiML activities as described above are required to comply with this CoC. Reviews should actively avoid subtle discrimination, however inadvertent. In particular, reviewers should avoid comments in reviews about English style or grammar that may be interpreted as implying that the author is “foreign” or “non-native”. Sponsors are equally subject to this CoC. In particular, sponsors should not use images, activities, or other materials that reinforce gender stereotypes or are of a sexual, racial, or otherwise offensive nature at WiML events. Booth staff, including but not limited to volunteers, should not create a sexualized environment. Unacceptable Behavior WiML is dedicated to providing an experience for all participants that is free from harassment, bullying, discrimination, and retaliation. This includes offensive comments related to gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, religion (or lack thereof), politics, technology choices, or any other personal characteristics or considerations made unlawful by federal, state, or local laws, ordinances, or regulations. Inappropriate or unprofessional behavior that interferes with another participant’s full participation will not be tolerated. This includes bullying, intimidation, personal attacks, harassment, sustained disruption of talks or other events, sexual harassment, stalking, following, harassing photography or recording, inappropriate physical contact, unwelcome sexual attention, public vulgar exchanges, derogatory name-calling, or diminutive characterizations, all of which are unwelcome in this community. Advocating for, or encouraging, any of the above behaviour, is also considered harassment. No use of images, activities, or other materials that are of a sexual, racial, or otherwise offensive nature that may create an inappropriate or toxic environment is permitted. Disorderly, boisterous, or disruptive conduct including but not limited to fighting, coercion, theft, damage to property, or any mistreatment or non-businesslike behavior towards other participants is not tolerated. Scientific misconduct—including but not limited to fabrication, falsification, or plagiarism of paper submissions or research presentations—is prohibited. Reporting If you have concerns related to your participation or interaction at a WiML activity, observe someone else’s difficulties, or have any other concerns you wish to share, you can make a report: Anytime: By email at codeofconduct@wimlworkshop.org ‬ During an event: In-person to organizers, volunteers, or any member of the WiML Board of Directors. They will then direct you to the designated responder(s) for that event. Organizers and volunteers can be identified by special badges marked as “ORGANIZER” or “VOLUNTEER”. Members of the WiML Board of Directors can be identified by special badges marked as “WiML Board”. There is no deadline by which to make a report. If the person receiving your report is not the designated responder for that event, they will direct you to a designated responder and/or provide you immediate medical or security help and assist you to feel safe for the duration of the activity. Designated responders will follow WiML procedures to respond to and investigate your report. Enforcement Any participant asked by any member of the community to stop any unacceptable behavior is expected to comply immediately. A response of “just joking” will not be accepted; behavior can be harassing without an intent to offend. If a participant engages in behaviour that violates this CoC, WiML retains the right to take any action deemed appropriate, including but not limited to: Formal or informal warnings Barring or limiting continued attendance and participation, including but not limited to expulsion from the event Barring from participating in or deriving benefits from future WiML activities Exclusion from WiML opportunities, e.g. leadership, organizing, volunteering, speaking, reviewing, sponsoring, etc. Reporting the incident to the offender’s local institution or funding agencies Reporting the incident to local law enforcement The same actions may be taken toward any individual who engages in retaliation or who knowingly makes a false allegation of harassment. If action is taken, an appeals process will be made available. Investigation Reports of violations will be handled at the discretion of the WiML Board of Directors, who will investigate reports and bring the issue to resolution. Reports made during the activity will be responded to within 24 hours; reports made at other times will be responded to in less than five weeks. All reports will be handled as confidentially as possible and information will be disclosed only as it is necessary to complete the investigation and bring to resolution. There may be situations (such as those involving Title IX issues in the United States and venue- or employer-specific policies) where the member of the WiML Board of Directors informed of the violation will be under an obligation to file a report with another individual or organization outside of WiML. Ongoing Review The WiML Board of Directors welcomes feedback from the community on this CoC policy and procedures; please contact us by email at info@wimlworkshop.org . Acknowledgements This CoC policy was written by adapting the wording and structure from other CoC policies and procedures by Geek Feminism Wiki (created by the Ada Initiative), NeurIPS , ACM , Montreal AI Symposium , and Deep Learning Indaba .

2nd Women in Machine Learning Un-Workshop The 2nd WiML virtual Un-Workshop is co-located with virtual ICML on Wednesday July 21st, 2021. Speakers Logistics Program Call for Participation Committee FAQ Code Of Conduct Machine learning is one of the fastest growing areas of computer science research. Search engines, text mining, social media analytics, face recognition, DNA sequence analysis, speech and handwriting recognition, healthcare analytics are just some of the applications in which machine learning is routinely used. In spite of the wide reach of machine learning and the variety of theory and applications, it covers, the percentage of female researchers is lower than in many other areas of computer science. Most women working in machine learning rarely get the chance to interact with other female researchers, making it easy to feel isolated and hard to find role models. The annual Women in Machine Learning Workshop is the flagship event of Women in Machine Learning . This technical workshop gives female faculty, research scientists, and graduate students in the machine learning community an opportunity to meet, network and exchange ideas, participate in career-focused panel discussions with senior women in industry and academia and learn from each other. Underrepresented minorities and undergraduates interested in machine learning research are encouraged to attend. We welcome all genders; however, any formal presentations, i.e. talks and posters, are given by women. We strive to create an atmosphere in which participants feel comfortable to engage in technical and career-related conversations. The workshop started at the 2006 Grace Hopper Celebration and moved to NeurIPS in 2008. A History of WiML poster was created in 2015 to celebrate the 10th workshop. This is the 2nd WiML Un-Workshop and is co-located with ICML . This event along with ICML are virtual events due to COVID-19. The term “un-workshop” is based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. The overall goal of the un-workshop is to advance research through collaboration and increased interaction among participants from diverse backgrounds. Different from the workshop, the un-workshop’s main focus is topical breakout sessions, with short invited talks and casual, informal poster presentations. Besides this un-workshop and annual workshop which is co-located with NeurIPS, Women in Machine Learning also organizes events such as lunch at AAAI conference, maintains a public directory of women active in ML, profiles the research of women in ML, and maintains a list of resources for women working in ML. Invited Speakers Celia Cintas Research Scientist, IBM Research - Nairobi Yingzhen Li Lecturer at Department of Computing. Imperial College London, UK Sarah Hooker Research Scientist at Google Brain Luciana Benotti Associate Professor at the Universidad National de Cordoba (UNC) Argentina Location This un-workshop takes place virtually due to COVID-19. Please check the program book for a complete overview of the program. Rocket.chat info desk and tech support If you have general questions or technical difficulties on the day of the event, drop by the Rocket.chat window on the workshop page on icml.cc . Best Practices for virtual events Virtual conferences can be tricky, and there are a lot of unintuitive ways to make your experience (and the experience of others) a little better. You can read some of our tips here . Information on Talks, Panel and Breakout Sessions We will be hosting the talks, panel as a Zoom webinar. We will also host breakout sessions on Zoom. You can join these sessions by clicking the links on the ICML Un-Workshop webpage . As an attendee, you will not be able to unmute yourself. If you have questions about the content of the talk, please submit the questions using the Zoom Q&A feature. Time permitting, and depending on the volume of questions, the moderator will either ask your question for you or confirm with you to ask the question yourself and unmute you at a suitable time. Note that Q&A will be moderated by us so you will only be able to see some of the questions of the other attendees. If you want to send messages to the moderators during the seminar, please use the Zoom chat feature. If you have not used Zoom before, we highly recommend downloading and installing the Zoom client before the meeting. Additional instructions on how to use Zoom during a webinar can be found here . Information on Poster Session and Mentorship Social The WIML Un-Workshop poster session, mentorship social and The Joint Affinity Groups Poster Session takes place in Gather.Town. You can join these sessions by clicking the links on the ICML Un-Workshop webpage . See Gather.Town guidelines to troubleshoot common access issues. If you face any issues, check these common video/audio issues or Gather.Town FAQ . An Important Note on ICML Registration Please note that the application form does not constitute registration for the WiML Un-Workshop. To attend the un-workshop, you need to register for ICML at https://icml.cc . There is no separate registration for the un-workshop. PROGRAM PANELISTS MENTORS ACCEPTED POSTERS The 2021 WiML Un-Workshop at ICML will be held virtually on Wednesday, July 21th, 2021. WiML will also participate in the ICML Affinity Groups Joint Poster Session with Queer in AI on Monday, July 19th. All participants are required to abide by the WiML Code of Conduct . Please use this link to access the Un-Workshop on ICML. Wednesday, July 21th, 2021 Time (ET/New York ) - Event 09:40 – 09:50: Introduction and Opening Remarks 09:50 – 10:00: WiML D&I Chairs Remarks 10:00 – 10:25: Invited talk – Yingzhen Li 10:25 – 11:30: Breakout sessions #1 11:30 – 12:00: Virtual Coffee Break and Poster Session #1 12:00 – 12:25: Invited Talk – Celia Cintas 12:25 – 13:30: Breakout Sessions #2 13:30 – 14:30: Sponsor Expo: Presentations by Microsoft, QuantumBlack, Apple, and Facebook 14:30 – 15:30: Mentoring Social 15:30 – 18:45: Break + Informal Social 18:45 – 19:25: Invited Talk – Sara Hooker 19:25 – 20:30: Breakout Sessions #3 20:30 – 21:00: Virtual Coffee Break and Poster Session #2 21:00 – 21:25: Invited Talk – Luciana Benotti 21:25 – 22:30: Breakout Sessions #4 22:30 – 23:30: Panel Discussion – Sarah Dean, Sarah Aerni, Sylvia Herbert, Kalesha Bullard, Amy Zhang (moderator) 23:30 – 23:45: Closing Remarks Our sponsor booths are open during the Un-Workshop. Please find information on their schedules and events here . For more details about the breakout sessions (e.g. affiliations), please use this link . You can submit your questions to the panelists through this link . Breakout session #1, 10:25 AM – 11:30 AM ET ID - Session title - Leaders - Facilitators 1.1 Catching Out-of-Context Misinformation with Self-supervised LearningShivangi AnejaMamatha Thota, Vishwali Mhasawade 1.2 School mapping using computer vision technologySafa SulimanMaryam Daniali 1.3 Data Integration and Predictive Modeling for Precision Medicine in OncologyMehreen Ali Esther Oduntan 1.4 Unsupervised Learning in Computer VisionAyca Takmaz, Clara Fernandez Labrador Naina Dhingra 1.5 Machine Learning for Privacy: An Information Theoretic PerspectiveEcenaz Erdemir, Fatemehsadat Mireshghallah Cemre Cadir 1.6 Fundamentals of Contrastive Learning in VisionSamrudhdhi Rangrej, Ibtihel Amara, Zahra Vaseqi Farzaneh Askari 1.7 Exploring probabilistic sparse inferencing through the triangulation of neuroscience, computing and philosophyGagana B, Stuti Gupta Akash Smaran 1.8 Neural Machine Translation for Low-Resource LanguagesEn-Shiun Annie Lee, Surangika Ranathunga, Rishemjit Kaur, Marjana Prifti SkenduliNiti M KC, Jivat Neet Kaur Breakout session #2, 12:25 PM – 1:30 PM ET ID - Session title - Leaders - Facilitators 2.1 Geometry and Machine LearningMelanie WeberAnkita Shukla 2.2 Leveraging Open-Source Tools for Natural Language ProcessingJennifer Glenskii RanaAneri Rana, Niti M KC 2.3 Challenges and Opportunities in ML for Health Care: How to address interpretability in clinical decision making?Annika Marie Schoene, En-Shiun Annie Lee, Peiyuan Zhou Malinda Vania 2.4 Leading the Way for the Next Generation of Black Women in STEMLouvere Walker-Hannon, Dr. Tracee Gilbert Mozhgan Saeidi 2.5 Un-bookclub Algorithms of OppressionRajasi Desai, Esther Oduntan, Anoush Najarian Sindhuja Parimalarangan 2.6 Research within community: how to cultivate a nurturing environment for your researchRosanne LiuMehreen Ali 2.7 Explainable machine learning: do we have the right tools?Michal Moshkovitz, Chhavi Yadav Shreya Ghosh 2.8 Decision-Making in Social Settings: Addressing Strategic Feedback EffectsMeena Jagadeesan, Celestine Mendler-Dünner Frances Ding Breakout session #3, 7:25 PM – 8:30 PM ET ID - Session title - Leaders - Facilitators 3.1 Does your model know what it doesn’t know? Uncertainty estimation and out-of-distribution (OOD) detection in deep learningJie Ren, Polina Kirichenko, Sharon Yixuan Li, Sergul Aydore, Haleh Akrami Liyan Chen 3.2 ML Applications in Big CodeSonia Kim, Mozhgan Saeidi Shima Shahfar 3.3 Connecting Novel Perspectives on GNNs: A Cross-Domain OverviewIlke Demir, Nesreen Ahmed, Vasuki Narasimha Swamy, Subarna Tripathi Ancy Tom 3.4 Bridging the gap between academia and industryChip Huyen, Sharon Zhou Sasha Luccioni 3.5 Variational Inference for Neural NetworksSahar Karimi, Audrey Flower Gargi Balasubramaniam 3.6 Responsible AI in production: Technical and Ethical considerationsParul Pandey, Himani Agrawal Wanda Wang Breakout session #4, 9:25 PM – 10:30 PM ET ID - Session title - Leaders - Facilitators 4.1 AI and Creativity: Approaches to Generative ArtAneta NeumannAncy Tom 4.2 Attrition of women and minoritized individuals in AIJeff Brown, Christine Custis, Madu Srikumar, Himani AgrawalJeff Brown, Christine Custis, Madu Srikumar 4.3 Safely navigating scalability-reliability trade-offs in ML methodsRuqi Zhang, A. Feder CooperMonica Munnangi Sponsor Expo Presentations, 1:30 PM – 2:30 PM ET Time (ET/New York ) - Sponsor - Speaker - Title 13:30 – 13:45 Microsoft Jennifer Neville Improving Productivity with Graph ML over Content-Interaction Networks 13:45 – 14:00 Quantum Black Viktoriia Oliinyk Algorithmic Fairness: Machine Learning with a Human Face 14:00 – 14:15 Apple Lizi Ottens Machine Learning at Apple 14:15 – 14:30 Facebook Ning Zhang Future of AI-Powered Shopping Mentorship Social, 2:30 PM – 3:30 PM ET ID - Mentor - Topic 1 Dina Obeid (Harvard) A non-linear career path in Machine Learning 2 Shakir Mohamed (DeepMind) Socio-Technical AI Research 3 Been Kim (Google Brain) Industry Research and Managing Up 4 Anna Goldenberg (U Toronto) Two body problem in academia, Raising a family, Grant strategies, Looking for a job to deploying ML in a hospital setting 5 Lalana Kagal (MIT) Maintaining work-life balance 6 Angelique Taylor (Cornell University) Transitioning from PhD to Assistant Professor Invited talk: Celia Cintas Towards fairness & robustness in machine learning for dermatology Abstract: Recent years have seen an overwhelming body of work on fairness and robustness in Machine Learning (ML) models. This is not unexpected, as it is an increasingly important concern as ML models are used to support decision-making in high-stakes applications such as mortgage lending, hiring, and diagnosis in healthcare. Currently, most ML models assume ideal conditions and rely on the assumption that test/clinical data comes from the same distribution of the training samples. However, this assumption is not satisfied in most real-world applications; in a clinical setting, we can find different hardware devices, diverse patient populations, or samples from unknown medical conditions. On the other hand, we need to assess potential disparities in outcomes that can be translated and deepen in our ML solutions. In this presentation, we will discuss how to evaluate skin-tone representation in ML solutions for dermatology and how we can enhance the existing models’ robustness by detecting out-out-distribution test samples (e.g., new clinical protocols or unknown disease types) over off-the-shelf ML models. Invited talk: Yingzhen Li Evaluating approximate inference for BNNs Abstract:Bayesian Neural Network is one of the major approaches for obtaining uncertainty estimates for deep learning models. Key to the success is the selection of the approximate inference algorithms used to compute the approximate posterior, with mean-field variational inference (MFVI) and MC-dropout being the most popular variants. But is the good downstream uncertainty estimation performance of BNNs attributed to good approximate inference? In this talk I will discuss some of our recent results towards answer this question. I will also discuss briefly the computational reasons of the preference of MFVI/MC-dropout and describe our latest work to make BNNs more memory efficient. Invited talk: Sara Hooker Characterizing the Generalization Trade-offs Incurred By Compression Abstract: To-date, a discussion around the relative merits of different compression methods has centered on the trade-off between level of compression and top-line metrics such as top-1 and top-5 accuracy. Along this dimension, compression techniques such as pruning and quantization are remarkably successful. It is possible to prune or heavily quantize with negligible decreases to test-set accuracy. However, top-line metrics obscure critical differences in generalization between compressed and non-compressed networks. In this talk, we will go beyond test-set accuracy and discuss some of my recent work measuring the trade-offs between compression, robustness and algorithmic bias. Characterizing these trade-offs provide insight into how capacity is used in deep neural networks — the majority of parameters are used to represent a small fraction of the training set. Formal auditing tools like Compression Identified Exemplars (CIE) also catalyze progress in training models that mitigate some of the trade-offs incurred by compression. Invited talk: Luciana Benotti Errors are a crucial part of dialogue Abstract: Collaborative grounding is a fundamental aspect of human-human dialogue which allows people to negotiate meaning; in this talk, I argue that current deep learning approaches to dialogue systems don’t deal with it adequately. Making errors, and being able to recover from them collaboratively, is a key ingredient in grounding meaning, but current dialogue systems can’t do this. I will illustrate the pitfalls of being unable to ground collaboratively, discuss what can be learned from the language acquisition and dialog systems literature, and reflect on how to move forward. I will urge the community to proceed by addressing a research gap: how clarification mechanisms can be learned from data. Novel research methodologies which highlight the importance of the role of clarification mechanisms are needed for this. I will present an annotation methodology, based on a theoretical analysis of clarification requests, which unifies a number of previous accounts. Dialogue clarification mechanisms are an understudied research problem and a key missing piece in the giant jigsaw puzzle of natural language understanding. I will conclude this talk with an open call for collaborators that share the vision presented. WiML Accepted Posters in Poster Session s (11:30 AM – 12:00 PM ET and 20:30 PM – 21:00 PM ET) and Joint Affinity Poster Session on Gather.Town (Monday 19 Jul 9:00 PM — 11:00 PM ET) Machine Learning Applications in Animal Sciences A mbreen Hamadani* (PhD Scholar, Animal Genetics and Breeding, SKUAST-K), Nazir A Ganai (Professor, Animal Genetics and Breeding, SKUAST-K) Emulating Aerosol Microphysics with Machine Learning Paula Harder* (University of Kaiserslautern) Duncan Watson-Parris (University of Oxford), Domink Strassel (Fraunhofer ITWM), Nicolas Gauger (University of Kaiserslautern), Philip Stier (University of Oxford), Janis Keuper (Offenburg University) Network Experiment Design for estimating Direct Treatment Effects Zahra Fatemi*(University of Illinois at Chicago), Elena Zheleva (Universty of llinois at Chicago) Adversarial Robust Model Compression using In-Train Pruning Manoj Rohit Vemparala (BMW Group), Nael Fasfous (Technical University of Munich), Alexander Frickenstein (BMW Group), Sreetama Sarkar* (BMW Group), Qi Zhao (Karlsruhe Institute of Technology), Sabine Kuhn (BMW Group), Lukas Frickenstein (BMW Group), Anmol Singh (BMW Group), Christian Unger (BMW), Naveen Shankar Nagaraja (BMW Group), Christian Wressnegger (Karlsruhe Institute of Technology), WALTER STECHELE (Technical University of Munich) Iterative symbolic regression for learning transport equations Mehrad Ansari*, Heta A. Gandhi*, David Foster, Andrew D. White; Department of Chemical Engineering, University of Rochester, Rochester, NY 14627 Cost Aware Asynchronous Multi-Agent Active Search Arundhati Banerjee*(School of Computer Science,Carnegie Mellon University), Ramina Ghods (School of Computer Science, Carnegie Mellon University), Jeff Schneider (School of Computer Science, Carnegie Mellon University) Exploration and preference satisfaction trade-off in reward-free learning Noor Sajid (WCHN, U CL), Panagiotis Tigas (OATML, Oxford University), Alexey Zakharov (Huawei, Imperial College), Zafeirios Fountas (Huawei, WCHN, UCL), Karl Friston (WCHN, UCL) HYBRIDNET: A NETWORK THAT LEVERAGES ON CLASSICAL AND NON-CLASSICAL COMPUTER VISION TECHNIQUES FOR FEW SHOT LEARNING ON INFRARED IMAGERY Maliha Arif * (PhD Candidate, Center for Research in Computer Vision – UCF) , Abhijit Mahalanobis ( Associate Professor, Center for Research in Computer Vision – UCF) Reinforcement Learning from Formal Specifications Kishor Jothimurugan (University of Pennsylvania), Suguman Bansal* (University of Pennsylvania), Obsert Bastani (University of Pennsylvania), Rajeev Alur (University of Pennsylvania) Clustering With Financial Fundamentals Jennifer Glenski* (Georgia Institute of Technology), Sara Srivastav (Georgia Institute of Technology), Rachel Riitano (Georgia Institute of Technology), Blake Heimann (Georgia Institute of Technology), Jenil Patel (Georgia Institute of Technology) Application of Knowledge Graph in Industry Samira Korani Contrastive Domain Adaptation Mamatha Thota(University of Lincoln), Georgios Leontidis(University of Aberdeen) Risk Analytics for Renewal of Purchase OrdersRisk Analytics for Renewal of Purchase Orders Shubhi Asthana (IBM Research), Pawan Chowdhary(IBM Research), Taiga Nakamura(IBM Research), Roberta Fadden (IBM) On the (Un-)Avoidability of Adversarial Examples Sadia Chowdhury* (York University), Ruth Urner (Assistant Professor, EECS Department, York University) Extraction of Adverse Drug Reactions from Tweets using Aspect Based Sentiment Analysis Sukannya Purkayastha (TCS Innovation Labs, Kolkata) Interpretation and transparency in acoustic emotion recognition Sneha Das* (Technical University of Denmark), Nicole Nadine Lønfeldt (Child and Adolescent Mental Health Center, Copenhagen University Hospital, Capital Region), Anne Katrine Pagsberg (Child and Adolescent Mental Health Center, Copenhagen University Hospital, Capital Region & Faculty of Health, Department of Clinical Medicine, Copenhagen University), Line H. Clemmensen (Technical University of Denmark) Seasonal forecasts of New Zealand’s local climate conditions with limited GCM inputs using Convolutional Neural Networks Fareeda Begum*(University of Canterbury), Varvara Vetrova (University of Canterbury), Nicolas Fauchereau (NIWA), Eibe Frank (University of Waikato), Tiger Xu(University of Waikato) Assessing the Carbon Intensity of Models Across Different Languages Gauri Gupta [1] (Manipal Institute of Technology), Krithika Ramesh* [1](Manipal Institute of Technology), Mirza Yusuf [1] (Manipal Institute of Technology), Praatibh Surana [1](Manipal Institute of Technology) (Equal contribution for all) A Low-rank Support Tensor Network Kirandeep Kour, Dr. Sergey Dolgov (University of Bath, UK), Prof. Dr. Martin Stoll (TU Chemnitz, Germany), Prof. Dr. Peter Benner (Max Planck Institute and TU Chemnitz, Germany) CricNet : Segment and Classify Cricket Events Sai Siddhartha Maram, Shambhavi Mishra*(Guru Gobind Singh Indraprastha University) Episodically optimized dynamical networks for robust motor control Sruti Mallik(*) (Electrical & Systems Engineering, Washington University in St Louis), ShiNung Ching (Electrical & Systems Engineering, Biomedical Engineering, Washington University in St. Louis) Open Set Detection via Similarity Learning Sepideh Esmaeilpour* (University of Illinois at Chicago), Lei Shu (Amazon AWS AI), Bing Liu(University of Illinois at Chicago) A modified limited memory Nesterov’s accelerated quasi-Newton *S. Indrapriyadarsini (Shizuoka University), Shahrzad Mahboubi (Shonan Institute of Technology), Hiroshi Ninomiya (Shonan Institute of Technology), Takeshi Kamio (Hiroshima University), Hideki Asai (Shizuoka University) Time-series Forecasting of Ionospheric Space Weather using Ensemble Machine Learning Randa Natras* (Technical University of Munich, Germany), Michael Schmidt (Technical University of Munich, Germany) SocialBERT : An Effective Few Shot Learning Framework Applied to a Social TV Setting Debarati Das* (Department of Computer Science, University of Minnesota Twin Cities), Roopana Chenchu (Department of Computer Science, University of Minnesota Twin Cities), Maral Abdollahi (Hubbard School of Journalism, University of Minnesota, Twin Cities), Jisu Huh (Hubbard School of Journalism, University of Minnesota, Twin Cities) and Jaideep Srivastava (Department of Computer Science, University of Minnesota Twin Cities) Explainable Prediction of Text Complexity: The Missing Preliminaries for Text Simplification Cristina Garbacea (University of Michigan Ann Arbor), Mengtian Guo (University of North Carolina at Chapel Hill), Samuel Carton (University of Colorado Boulder), Qiaozhu Mei (University of Michigan Ann Arbor) Alignment of Language Agents in V ideogames Gema Parreno ( Mempathy ) Using Weak Supervision to Identify Drug Mentions from Class Imbalanced Twitter Data Ramya Tekumalla* (Georgia State University), Juan M Banda (Georgia State University)) Call for Participation The 2nd WiML Un-Workshop is co-located with ICML on Wednesday, July 21st, 2021. The Women in Machine Learning will be organizing the second “un-workshop” at ICML 2021. This is an event format to encourage more participant interaction, especially with ICML going virtual this year. The un-workshop is based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. Different from the workshop, the un-workshop’s main focus is topical breakout sessions, with short invited talks and casual, informal poster presentations. The overall goal of the un-workshop is to advance research through collaboration and increased interaction among participants from diverse backgrounds. Students, postdocs and researchers in all areas of Machine Learning who primarily identify as a woman and/or nonbinary are encouraged to submit one-page proposal to lead a breakout session on a certain research topic. While all presenters will identify primarily as a woman and/or nonbinary, all genders are invited to attend. Important dates June 14th, 2021 – Application form opens July 4th, 2021 – Deadline (anywhere on Earth) to apply for a breakout session, poster, registration fee funding, facilitating or volunteering July 10th, 2021 – Notification of acceptance of breakout session’s proposals July 10th, 2021 – Notification of acceptance of posters, registration fee funding, facilitators, volunteers July 21st, 2021 – WiML Un-Workshop Day Various ways of participating in WiML un-workshop Lead a breakout session: submit a proposal to lead a breakout session on a certain research topic. Facilitate a breakout session: assist breakout session leaders by taking notes and encouraging participant interactions and taking attendance. Present a poster: present a poster in a casual, informal setting. Volunteer: help with technical setup and in-event needs. Attend: participate in breakout session discussions. Breakout session proposals A breakout session is a 1-hour free-form discussion overseen by 1-3 leaders and with assistance from 1-2 facilitators to take notes and encourage participant interactions. We strongly encourage students, postdocs, and researchers who primarily identify as women and/or nonbinary in all areas of machine learning to submit a proposal to lead a topical breakout session. A complete proposal consists of a 1 page blind PDF (example here ) and the names and bios of leaders submitted separately in the application form. We strongly recommend having at least 2 leaders, with a diverse set of leaders preferred (see selection criteria below). The names of facilitators can also be provided if known at submission time. Otherwise, the organizers will match facilitators to breakout sessions. Breakout session leaders must identify primarily as women and/or nonbinary; facilitators can be of any gender. Only one proposal submission per leader is allowed. If there are multiple leaders, only one leader needs to submit the proposal. There are no proceedings. WiML registration fee funding is prioritized for accepted breakout session leaders who fulfill certain eligibility criteria (see details below) and do not have any other sources of funding. Breakout session guidelines: Role of leaders: Point-out key characteristics of your topic and make connections with other topics. Describe the key challenges in this research area on a high-level. Describe the key approaches on a high-level to provide intuition. Highlight possible points of discussion/goals to achieve during the session. Use graphics/imagery and materials e.g. slides as needed Encourage inclusive (rather than unilateral) discussions Role of facilitators: take notes and encourage participant interactions. Leaders and facilitators should anticipate a small additional time commitment before the un-workshop to receive briefing/training and a possible dry run. While the exact technology is still being determined, we anticipate using video-conferencing software (e.g. Zoom). Submission instructions for breakout sessions: Proposals must be no more than 1 page (including any references, tables, and figures) submitted as a PDF. Main body text must be minimum 11 point font size and page margins must be minimum 0.75 inches (all sides). Your proposal should stand alone, without linking to a longer paper or supplement. You should provide a brief description of the topics you’d like to discuss, any relevant references, a plan for how you’d organize the time (1 hour) allocated for a session, as well as some ideas on how you’d encourage discussion and participant interaction during the session. The PDF must not include identifying information, as it will be reviewed blind. In particular, the PDF should not contain information of the leaders or facilitators. Instead, submit their information in the application form. Selection criteria for breakout sessions: The degree to which it is expected that participants will find the topic interesting and valuable. Diversity of leaders and facilitators, including diversity of experience/seniority, affiliation, race, viewpoint and thinking regarding the topic, etc. Plans for encouraging discussion and participant interaction during the session. Facilitators If you are interested in facilitating a breakout session but have not yet connected with anyone submitting a breakout session proposal, you can indicate your interest in the application form. Organizers will match selected facilitators to breakout sessions. Facilitators should anticipate a small additional time commitment before the un-workshop to receive briefing/training and a possible dry run. Posters If you wish to present a poster, submit EITHER a short abstract (max 1500 characters) OR a PDF of the poster (only if you have it already). The poster may describe new, previously, concurrently published, or work-in-progress research. Posters in theory, methods, and applications are welcome. The poster presenter must identify primarily as a woman and/or nonbinary; other authors can be of any gender. The poster presenter does not need to be the first author of the work. Only one poster submission per presenter is allowed. Accepted posters will be presented in a casual, informal setting. This setting is very different from formal poster sessions, e.g. at WiML Workshop at NeurIPS. While the exact presentation format is still being determined, it may be as simple as a webpage with poster PDF and pre-recorded video. There are no oral or spotlight presentations. There are no proceedings. Submission instructions for posters: Submitted materials may contain identifying information, as posters for this un-workshop are not reviewed blind. Your submission should stand alone, without linking to a longer paper or supplement. You should convey motivation and give some technical details of the approach used. While we acknowledge that space is limited, some experimental results are likely to improve reviewers’ opinions of your poster. Registration fee funding The virtual nature of ICML and this un-workshop allows individuals from all over the world to attend. By funding a number of ICML registrations, WiML hopes to further expand the range of participants at this un-workshop. To apply for funding, you should: identify primarily as a woman and/or nonbinary; be a student, postdoc, or have an equivalent position (equivalent positions include unemployed recent grads and early career researchers from underrepresented geographical regions). Accepted breakout session leaders who fulfill the above eligibility criteria and do not have any other sources of funding will be prioritized for WiML funding. Other participants are also encouraged to apply. Priority will be given to individuals from underrepresented regions or groups, first-time attendees of ICML or similar conferences, and individuals who would benefit the most from this funding. Funding recipients must participate in at least one breakout session as a leader, facilitator, or attendee. Due to limited funding, we may not be able to support everyone eligible; however, we hope to support as many eligible applicants as possible. We also encourage you to apply for ICML volunteer and funding opportunities, which are separate and independent of WiML funding. Check the ICML website directly for details. Volunteering We are seeking volunteers to help with technical setup and virtual technology testing before the event, as well as help during the event, e.g. letting people into Zoom rooms, etc. We may also need emergency reviewers for breakout session proposals. You can indicate if you can help in any way in the application form here . Participation instructions To participate in ANY of the above roles and/or apply for registration fee funding, please fill in this application form by **July 4, 2021**. Selected breakout session leaders, facilitators, poster presenters, volunteers, and funding recipients will be notified individually by the dates mentioned above. If you only wish to attend, we still recommend you fill in this form to provide your timezone and topic preferences. All participants are required to abide by the WiML Code of Conduct . Important note: This form does not constitute registration for the WiML Un-Workshop. To attend the un-workshop, you need to register for ICML at https://icml.cc . Submission is now open! Organizers Beliz Gokkaya, Facebook Wenshuo Guo, University of California, Berkeley Arushi Majha, University of Cambridge Liyue Shen, Stanford Olivia Choudhury, Amazon Berivan Isik, Stanford Hadia Mohmmed Osman Ahmed Samil, Mila Vaidheeswaran Archana, Continental Automotive Questions? Check out the FAQs or reach us at workshop[at]wimlworkshop[dot]org PLATINUM SPONSORS Committee ORGANIZERS Beliz Gokkaya Software Engineer at Facebook, General Chair Wenshuo Guo PhD Student at University of California, Berkeley, Program Chair Hadia Mohmmed Osman Ahmed Samil Breakout Program and Logistics Co-Chair Berivan Isik PhD Student at Stanford University, Breakout Program and Logistics Co-Chair Olivia Choudhury Researcher at Amazon, Senior Program and Networking Chair Arushi Majha PhD Student at University of Cambridge, Finance and Sponsorship Chair Liyue Shen PhD Student at Stanford University, Funding and Volunteers Chair Vaidheeswaran Archana AI Engineer at Continental Automotive, Virtual Experience Chair Diversity and Inclusion Chair Danielle Belgrave, Principal Research Manager at Microsoft Research Supervolunteers We would like to acknowledge and warmly thank our super-volunteers who worked tirelessly to ensure a high quality un-workshop. Belen Saldias, MIT Elre Oldewage, University of Cambridge Mandana Samiei, McGill and Mila Niveditha Kalavakonda, University of Washington Seattle Weiwei Zong, Henry Ford Health System FAQs How do I register for the un-workshop? You need to register to ICML to attend to WiML and then please fill the application form provided. Please refer to call for participation for more details. Is filling the application form enough for register to WiML? No, you need to register to ICML . What is an un-workshop? The un-workshop is based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. The overall goal of the un-workshop is to advance research through collaboration and increased interaction among participants from diverse backgrounds. How is an un-workshop different from WiML workshop at NeurlPS? WiML Workshop at NeurIPS is a one-day event with invited speakers, oral presentations, and posters. This year WiML is bringing a new event format to ICML to encourage more participant interaction, especially with ICML going virtual this year. Different from the workshop, the un-workshop’s main focus is topical breakout sessions, with short invited talks and casual, informal poster presentations. I'm a man. Can I attend WiML? Yes. All genders are welcome to attend! To do so, please register for ICML and fill the application form . Note, however, that all speakers, breakout session leaders and poster presenters will primarily identify as a woman and/or nonbinary, as our goal is to promote them and their work within the machine learning community. Where will the un-workshop take place? This is a virtual event. How much funding is available? Funding is distributed based on geographic location. Support varies from year to year and this year due to COVID-19, it will be a virtual event and ICML registration fee funding is available for participants who fulfill eligibility criteria. Is there a code of conduct? Yes. WiML requires all participants and reviewers to abide by our code of conduct . Is WiML an archival venue? No, WiML is a non-archival venue. This means that, if your contribution is accepted, we will not be asking you to submit a camera-ready version of it, nor will we publish it anywhere (neither online nor in proceedings of any sort). We will only make the title and authors’ names available in the program book. How can I get more information on un-workshop logistics? Please check out the logistics page! I want to support WiML by providing sponsorship / recruiting at the un-workshop. Who should I talk to? Thank you for your support! Please contact us . How can I join the WiML network? Join our Google Group . When and where do I submit my proposal? You can find more information on call for participation. Submission to the 2021 WiML un-workshop is now closed. How many breakout sessions will be on the day of the un-workshop? There are 4-time slots for 1-hour breakout sessions (marked as Breakout Sessions #1 to #4). Each of these 4-time slots will have several parallel breakout sessions. Why do breakout sessions involve Zoom and Slack? Zoom rooms are mainly for the breakout sessions for the specific one hour period. However, leaders can use Slack a few days before and after to ask participants to read some papers, ask them specific questions and keep the discussions going. Also, participants can ask questions regarding the breakout session’s topic in the Slack channel before the actual session. Can I make breakout rooms in the breakout session as a leader? Yes, leaders can make smaller breakout rooms to engage participants in smaller group discussions. How many attendees will be in each breakout session? We can’t promise the exact number but we are hoping for smaller groups (max 20) to increase interaction between participants. What is the whiteboard in Zoom rooms? Whiteboard is like a digital board and leaders and participants can write on it and explain a specific topic. More instructions are available here. Will we as leaders be given a chance to advertise our proposal topic before the un-workshop? Sure, you can advertise your session’s topic on Twitter for example and tag us on @WiMLworkshop and we can retweet that. Also, attendees will have access to the breakout session topics at least a week before the un-workshop. Can anyone who did not fill the WiML form still join the un-workshop? Anyone who is registered to ICML can join the un-workshop. I am new to the Gather.town platform being used for the live poster session. How can I prepare for it? Check out these guidelines. I have a question that's not answered here. How do I reach you? Contact us . Back To Top

19th Women in Machine Learning Workshop (WiML 2024) The workshop is co-located with NeurIPS on Tuesday, December 10th, 2024 at the Vancouver Convention Center in Vancouver, BC, Canada. 19th Women in Machine Learning Workshop (WiML 2024) — the workshop is co-located with NeurIPS on Tuesday, December 10th, 2024. For more information or to register, please visit the event’s website here. Back To Top

Do you have a list of members? How can I join WiML? WiML doesn’t have “members” per se, any women working in machine learning can be part of the WiML network. We have a mailing list for anyone to post announcements of interest to the WiML network and an opt-in, necessarily incomplete directory of women working in machine learning. How can I join the WiML mailing list? Join the mailing list directly here. What kind of events do you organize? Our flagship event is the annual WiML Workshop, typically co-located with NeurIPS, a machine learning conference. We also organize an “un-workshop” at ICML, as well as small events (e.g. lunches and receptions) at other machine learning conferences, such as CoRL, COLT, etc. Check out our events page for up-to-date listings of events. Do you have local meetups? No, but check out WiMLDS (website, Twitter), another organization that supports women in machine learning by organizing local meetups. How do I reach the WiML network? Use our mailing list. How can I sponsor WiML? Thank you for your interest in sponsoring WiML! See this page for more information. I am looking for an invited speaker/panelist/area chair/program committee member/ etc. Can WiML help me? Use our directory of women in machine learning or post this opportunity to our mailing list. I want to circulate a job posting. Can WiML help me? Post directly to our mailing list. How can I support WiML? You can: Post interesting opportunities and job postings to our mailing list. Use our directory of women in machine learning to find invited speakers, panelists, area chairs, program committee members, etc, or post these opportunities to our mailing list. Sponsor us. See this page for more information. Volunteer at one of our events. Check out our events page for up-to-date listings of events. Apply to be an area chair or reviewer at WiML Workshop (see this year’s workshop website for info). Take pictures at our events and share with us (tag @wimlworkshop on Twitter). If you see us mentioned in the media, send us a link at info@wimlworkshop.org. And many others! How did WiML start? What's the founding story? Hanna Wallach, Jennifer Wortman Vaughan, Lisa Wainer, and Angela Yu shared a room at NIPS 2005. Late one night, they talked about how exciting it was that there were FOUR female students at NIPS that year. They tried to list all the women in machine learning they know of and got to 10, then started talking about creating a meeting or gathering for all these women and perhaps others that they didn’t know about. Jenn, Lisa, and Hanna put together a proposal for a session at the 2006 Grace Hopper Celebration of Women in Computing that would feature talks and posters by female researchers and students in machine learning. The 1st WiML workshop was co-located with the 2006 Grace Hopper Celeberation. In 2008, WiML Workshop moved to NIPS (renamed NeurIPS in 2018) and there has been a WiML Workshop at NeurIPS every year since. In 2020, WiML introduced an “un-workshop” at ICML based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. Read more WiML history here! I have a question that isn't here. How do I reach you? We receive a lot of email. Help us help you by reaching out through the appropriate channels. Job posting, announcement, CFP, etc: Post directly to WiML mailing list. Have event pictures to share: post on Twitter and tag @wimlworkshop Workshop enquiries: workshop@wimlworkshop.org If you are a company interested in sponsoring WiML: sponsorship@wimlworkshop.org Any other enquiries: info@wimlworkshop.org If you email us, don’t cc multiple email addresses — this saves us time routing your email to one mailbox, and reduces the chances of your email getting lost. Thank you in advance! FAQ

16th Women in Machine Learning Workshop (WiML 2021) The 16th WiML Workshop is co-located with virtual NeurIPS on Thursday, December 9th and Friday, December 10th, 2021. Speakers Logistics Program Call for Participation Committee FAQ Code Of Conduct Machine learning is one of the fastest growing areas of computer science research. Search engines, text mining, social media analytics, face recognition, DNA sequence analysis, speech and handwriting recognition, healthcare analytics are just some of the applications in which machine learning is routinely used. In spite of the wide reach of machine learning and the variety of theory and applications, it covers, the percentage of female researchers is lower than in many other areas of computer science. Most women working in machine learning rarely get the chance to interact with other female researchers, making it easy to feel isolated and hard to find role models. The annual Women in Machine Learning Workshop is the flagship event of Women in Machine Learning. This technical workshop gives female faculty, research scientists, and graduate students in the machine learning community an opportunity to meet, network and exchange ideas, participate in career-focused panel discussions with senior women in industry and academia and learn from each other. Underrepresented minorities and undergraduates interested in machine learning research are encouraged to attend. We welcome all genders; however, any formal presentations, i.e. talks and posters, are given by women. We strive to create an atmosphere in which participants feel comfortable to engage in technical and career-related conversations. Now in its 16th year, the 2021 workshop is co-located virtually with NeurIPS . Besides this un-workshop and annual workshop which is co-located with NeurIPS, Women in Machine Learning also organizes events such as lunch at ICML and AAAI conferences, maintains a public directory of women active in ML, profiles the research of women in ML, and maintains a list of resources for women working in ML. Invited Speakers Sunita Sarawagi Professor, Indian Institute of Technology Bombay Meire Fortunato Research Scientist, Deepmind Adriana R Soriano Research Scientist, Facebook AI Research Adjunct Professor, McGill University Bo Li Assistant Professor, University of Illinois at Urbana–Champaign Jade Abbott Machine Learning Lead, Retro Rabbit Orevaoghene Ahia PhD Student, University of Washington Perez Ogayo Master Student, Carnegie Mellon University Location This workshop will be virtual. WiML Platform This year WiML would be on GatherTown platform. For detailed instruction, please check: https://www.gather.town/ PROGRAM PANELISTS MENTORSHIP ROUNDTABLES SPONSOR EXPO SOCIAL ACCEPTED POSTERS Thursday, December 9, 2021 WiML Workshop 1 (UTC time in 24 hour format) 2:00 – 3:00 Pre-Workshop Informal Social 3:00 – 3:20 Opening Remarks – WiML 2021 organizers 3:20 – 3:30 WiML D&I Chairs Remarks 3:30 – 4:15 Invited talk – Machine Learning as a Service: The Challenges of Serving diverse client Distributions, Sunita Sarawagi 4:15 – 4:40 Contributed talk #1 – Regret minimization in heavy-tailed bandits, Shubhada Agrawal 4:45 – 5:45 Poster Session #1 5:45 – 6:15 Break 6:15 – 7:00 Invited talk – Learning physics models that generalize, Meire Fortunato Friday, December 10, 2021 WiML Workshop 2 (UTC time in 24 hour format) 2:00 – 3:00 Speed Networking/Social 3:00 – 4:00 Social in Gather Town 4:00 – 5:05 Invited talk – The Unreasonable Effectiveness of Collaborative Research - The Masakhane Story, Jade Abbott, Perez Ogayo, Orevaoghene Ahia 5:05 – 5:30 Contributed talk #2 – Syntax-enhanced Dialogue Summarization, Seolhwa Lee 5:30 – 7:00 Social in Gather Town WiML Workshop 3 (UTC time in 24 hour format) 10:00 – 11:00 Speed Networking/Social 11:00 – 12:45 Mentorship Roundtables I | Sponsor Expo 12:45 – 13:45 Poster Session #2 | Sponsor Expo 13:45 – 13:50 Break 13:50 – 14:35 Invited talk – Seeing the unseen: Inferring unobserved information from limited sensory data, Adriana Romero-Soriano 14:35 – 15:00 Contributed talk #3 – Causal Meta-learning by Making Informative Interventions about the Functional Form Chentian Jiang WiML Workshop 4 (UTC time in 24 hour format) 19:00 – 19:45 Invited talk – Trustworthy Machine Learning via Logic Inference, Bo Li 19:45 – 20:10 Contributed talk #4 – A Graph Perspective on Neural Network Dynamics Fatemeh Vahedian 20:10 – 20:15 Break 20:15 – 21:55 Mentorship Roundtables II | Sponsor Expo 21:55 – 22:40 Panel Discussion: Career and Life 22:40 – 23:00 Closing Remarks Emily Denton Research Scientist Google Devi Parikh Research Director at Facebook AI Research & Associate Professor at Georgia Tech Adriana R Soriano Research Scientist, Facebook AI Research Adjunct Professor, McGill University Bo Li Assistant Professor, University of Illinois at Urbana–Champaign All participants are required to abide by the WiML code of conduct . Joint Affinity Groups Poster Session This poster session will be held jointly with other affinity workshops including Black in AI , LatinX in AI , Queer in AI , and Indigenous in AI . Poster IDs: J.001—J.190 Time: Tuesday Dec 7, 5:00 - 7:00 UTC Location: Joint Affinity Groups Poster Session Gather.Town WiML Poster Session #1 Poster IDs: W001—W040 Time: Thursday Dec 9, 4:45 - 5:45 UTC Location: WiML Gather.Town Poster Rooms 1 & 2 A listing of posters presented in this session can be found here . WiML Poster Session #2 Poster IDs: W041—W099 Time: Friday Dec 10, 12:45 - 13:45 UTC Location: WiML Gather.Town Poster Rooms 3, 4, & 5 A listing of posters presented in this session can be found here . WiML Poster Session #1: Poster Room 1 (W001 - W019) Identifying Hijacked Reviews Monika M Daryani*; James Caverlee Polaris: accurate spot detection for biological images with deep learning and weak supervision Emily C Laubscher*; Nitzan Razin; Will Graf; David Van Valen Feedforward Omnimatte Sharon Zhang*; Jonathan Huang; Vivek Rathod Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies Anaelia Ovalle* Identifying ATT&CK Tactics in Android Malware Control Flow Graph Through Graph Representation Learning and Interpretability Christine Patterson*; Jeffrey Fairbanks; Andres Orbe; Edoardo Serra; Marion Scheepers Self-Supervised Visual Representation Learning for Time-series Clustering Gaurangi Anand*; Richi Nayak A Data-driven Approach to Infer Latent Dynamics of COVID-19 Transmission Model Sujin Ahn*; Minhae Kwon Soil Moisture Estimation with cycleGANs for Time-series Gap Filing Natalia Efremova*; Mohamed-el-amine Seddik; Esra Erten Automated deep lineage tree analysis using a Bayesian single cell tracking approach Kristina Ulicna*; Giulia Vallardi; Guillaume Charras; Alan R Lowe Evaluating the Impact of Embedding Representations on Deception Detection Ellyn Ayton*; Maria Glenski SPP-EEGNET: An Input-Agnostic Self-supervised EEG Representation Model for Inter-Dataset Transfer Learning Xiaomin Li*; Vangelis Metsis Across the Pond and Back: Evaluation of News Deception Detection Approaches Across Natural and Synthetic Regional Dialects Robin Cosbey*; Maria Glenski Graph Convolutional Networks for Multi-modality Movie Scene Segmentation Yaoxin Li*; Alexander Wong; Mohammad Javad Shafiee Data Efficient Domain Adaptation using FiLM Sinjini Mitra*; Ankita Shukla; Rushil Anirudh; Jayaraman Thiagarajan; Pavan Turaga Deep Generative Models for Task-Based fMRI Analysis Daniela F de Albuquerque*; Jack Goffinet; Rachael Wright; John M Pearson Active Noise Cancellation for Spatial Computing Li Chen*; Purvi Goel; David Yang; Xiang Gao; Ilknur Kaynar Kabul Self-Supervision for Scene Graph Embeddings Brigit Schroeder*; Adam M Smith; Subarna Tripathi A Vision-Based Gait Analysis Framework for Predicting Multiple Sclerosis Rachneet Kaur*; Manuel Hernandez; Richard Sowers TaxonBags: Clustering and Vote for Precise Metagenomic Taxonomic Classification Induja Chandrakumar* WiML Poster Session #1: Poster Room 2 (W026 - W047) Gaussian Process Bandits with Aggregated Feedback Mengyan Zhang*; Russell Tsuchida; Cheng Soon Ong Privacy-Preserving Federated Multi-Task Linear Regression: A One-shot Linear Mixing Approach Inspired by Graph Regularization Harlin Lee* Comparative Analysis of Machine Learning Techniques for Breast Cancer Detection Jesutofunmi O Afolayan* Drought and Nitrogen Induced Stress Identification for Maize Crop using Deep Learning deployed on Unmanned Aerial Vehicles (Drones) Tejasri Nampally*; Ujwal Sai; Siddha Ganju; Ajay Kumar; Rajalakshmi Pachamuthu; Balaji Naik Banothu Scene statistics and noise determine the relative arrangement of receptive field mosaics Na Young Jun* Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks Yujun Yan*; Milad Hashemi; Kevin Swersky; Yaoqing Yang; Danai Koutra Learning Aerodynamics and Instrument behavior to Fly in Dangerous Conditions Cynthia Koopman*; David Zammit Mangion; Alexiei Dingli Reading the Road: Leveraging Meta-Learning to Learn Other Driver Behavior Anat Kleiman*; Ryan P Adams Commit-Checker: A human-centric approach for adopting bug inducing commit detection using machine learning models Naz Zarreen Oishie*; Banani Roy Using Embeddings to Estimate Peer Influence on Social Networks Irina Cristali*; Victor Veitch Mitigating Overlap Violations in Causal Inference with Text Data Lin Gui*; Victor Veitch Automatic Curricula via Expert Demonstrations Siyu Dai*; Andreas Hofmann; Brian Williams Leveraging Resource Allocation and Approximation for Faster Hyperparameter Exploration Shu Liu* The Many Hats We Wear as Machine Learning Practitioners for Marine Mammal Conservation Louisa van Zeeland*; Gracie Ermi Exploiting Hyperdimensional Computing and Probabilistic Inference for Reasoning Across Levels of Abstraction in Dynamic Biosignal-Based Applications Laura I Galindez Olascoaga*; Alisha Menon; Jan M. Rabaey Augment Your Deterministic Model with Monte Carlo Dropout to Combat Noisy Labels Li Chen*; Karen Chen; Purvi Goel; Ilknur Kaynar Kabul Occlusion-Aware Crowd Navigation Using People as Sensors Ye-Ji Mun*; Masha Itkina; Katherine Driggs-Campbell Physics-assisted Machine Learning Abhilasha Katariya*; Jin Ye; Dipal Gupta; Rohit Malshe; Chinmoy Mohapatra; Gautam Natarajan; Liron Yedidsion An Interpretable Approach to Hateful Meme Detection Tanvi M Deshpande*; Nitya Mani Solving the super rural and super dense delivery with asset-light programs Jin Ye*; Dipal Gupta; Abhilasha Katariya; Rohit Malshe; Natarajan Gautam; Liron Yedidsion; Chinmoy Mohapatra Model-Free Learning for Continuous Timing as an Action Helen Zhou*; David Childers; Zachary Lipton Accelerating Symmetric Rank 1 Quasi-Newton Method with Nesterov's Gradient Indrapriyadarsini Sendilkkumaar*; Shahrzad Mahboubi; Hiroshi Ninomiya; Takeshi Kamio; Hideki Asai WiML Poster Session #2: Poster Room 3 (W050 - W071) Generating Thermal Human Faces for Physiological Assessment using Thermal Sensor Auxiliary Labels Catherine Ordun*; Sanjay Purushotham; Edward Raff Machine Learning API in NASA’s WorldView Satellite Image Search System Kai E Priester*; Daniela Fragoso Syntax-enhanced Dialogue Summarization using Syntax-aware information Seolhwa Lee*; Kisu Yang; Chanjun Park; João Sedoc; Heuiseok Lim COVID-Net Clinical ICU: Enhanced Prediction of ICU Admission for COVID-19 Patients via Explainability and Trust Quantification Audrey Chung*; Mahmoud Famouri; Andrew Hryniowski; Alexander Wong Predictive classification of clinical ball catching trials with recurrent neural networks Jana Lang* Effectiveness of Transformers on Session-Based Recommendation Sara Rabhi*; Ronay Ak; Gabriel S P Moreira; Jeong Min Lee; Even Oldridge The impact of weather information on machine-learning probabilistic electricity demand predictions Yifu Ding*; Hannah Bloomfield; Malcolm McCulloch Social Representation of Political Inclination of Users Anjali Jha* Combining semantic search and twin product classification for recognition of purchasable items in voice shopping Dieu Thu Le; Verena Weber*; Melanie Bradford Sequential Decision Making with Limited Resources Hallee E Wong*; Maggie Makar; Aniruddh Raghu; John Guttag As easy as APC: Leveraging self-supervised learning in the context of time series classification with varying levels of sparsity and severe class imbalance Fiorella Wever*; Laura Symul; Victor Garcia; T. Anderson Keller Machine learning powered quantitative histologic assessment of disease severity in ulcerative colitis Kathleen Sucipto*; Archit Khosla; Fedaa Najdawi; Michael Drage; Maryam Pouryahya; Stephanie Hennek; Victoria Mountain; Murray Resnick; Amaro Taylor-Weiner; Deepta Rajan; Ilan Wapinski; Andy Beck Improved robustness to disfluencies in RNN-Transducer based Speech Recognition Tina Raissi*; Valentin Raissi; Manuel Giollo; Guglielmo Camporese ``We Don't Talk Anymore?": An analysis of cross-cutting political discussion on Reddit Dulshani Withana Thanthri Gamage* A Graph Perspective on Neural Network Dynamics Fatemeh Vahedian*; Ruiyu Li; Puja Trivedi; Di Jin; Danai Koutra How we browse: Measurement and analysis of digital behavior Yuliia Lut*; Michael Wang; Elissa M. Redmiles; Rachel Cummings Topological characterizations of neuronal fibers and its implications in comparing brain connectomes S.* Shailja; B.S. Manjunath Towards Automated Evaluation of Explanations in Graph Neural Networks Vanya BK*; Balaji Ganesan; Aniket Saxena; Devbrat Sharma; Arvind Agarwal Graph Representation Learning on Trajectory-Encoded Volumetric Heatmaps for Human Motion Generation Michelle Wu*; Zhidong Xiao; Hammadi Nait-charif Opening the Black Box: High-dimensional Safe Policy Search via Sim-to-real Aneri Muni* , Matteo Turchetta, Andreas Krause Transformer-based Self-Supervised Learning for Medical Images Mariia Dobko*; Mariia Kokshaikyna Fixed Neural Network Steganography: Train the images, not the network Varsha Kishore*; Xiangyu Chen; Yan Wang; Boyi Li; Kilian Weinberger WiML Poster Session #2: Poster Room 4 (W078 - W088) Regret Minimization in Heavy-Tailed Bandits Shubhada Agrawal*; Sandeep K Juneja; Wouter M Koolen Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning Siddha Ganju*; Sayak Paul Classification of Shoulder Impingement Syndrome using Transfer Learning model Raquel Marasigan* Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation Hanjie Chen*; Yangfeng Ji A Neural Network Ensemble Approach to System Identification Elisa Negrini*; Giovanna Citti; Luca Capogna Predicting Fake News and Real News Spreaders' Influence Amy Zhang*; Daniel Hammer; Aaron Brookhouse; Francesca Spezzano; Liljana Babinkostova Parkinson’s Disease Detection using Imputed Multimodal Datasets Hetvi Jethwani*; Bhumika Chopra How Much Data Analytics is Enough?: The ROI of Machine Learning Classification and its Application to Requirements Dependency Classification Gouri Deshpande*; Guenther Ruhe; Chad Saunder Propagation on Multi-relational Graphs for Node Regression Eda Bayram* Do You See What I See: Using Augmented Reality and Artificial Intelligence Shruti Karulkar*; Louvere Walker-Hannon; Sarah Mohamed Strategic Clustering Ana-Andreea Stoica*; Christos Papadimitriou WiML Poster Session #2: Poster Room 5 (W106 - W130) Interpretable Machine Learning with Symbolic Regression Aurélie Boisbunon*; Carlo Fanara; Ingrid Grenet; Jonathan Daeden; Alexis Vighi; Marc Schoenauer Clipping Range Methods in Proximal Policy Optimization Mónika Farsang* The Two-sample Problem in High Dimension: A Ranking-based Method Myrto Limnios*; Stephan Clémençon; Nicolas Vayatis Causal meta-learning by making informative interventions about the functional form Chentian Jiang*; Chris Lucas Maintenance planning framework using online and offline deep reinforcement learning Zaharah Bukhsh*; Nils Jansen; Hajo Molegraaf Machine Learning-based Mobility Assessment from Passively Sensed Digital Biomarkers Emese Sükei*; Pablo M Olmos; Antonio Artés Getting Started with Model Cards Maitreyi Chitale*; Anoush Najarian; Helen Chigirinskaya; Sindhuja Parimalarangan; Louvere Walker-Hannon; Rajasi Desai; Kyle Rawding; Brian Liu Interpretable & Hierarchical Topic Models using Hyperbolic Geometry Simra Shahid*; Tanay Anand; Sumit Bhatia; Nikaash Puri; Balaji Krishnamurthy; Nikitha Srikanth Fairness properties do not transfer: do we have viable solutions for real-world applications? Jessica Schrouff*; Natalie GHarris; Sanmi Koyejo; Ibrahim Alabdulmohsin; Eva Schnider; Krista Opsahl-Ong; Alex Brown; Subhrajit Roy; Diana Mincu; Christina Chen; Awa Chen; Yuan Liu; Vivek Natarajan; Katherine Heller; Alexander D'Amour Combining Transfer Learning And Transformer Attention Mechanism to Increase Aqueous Solubility Prediction Performance Magdalena Wiercioch*; Johannes Kirchmair Application of an interpretable graph neural network to predict gene expression in histopathological images Ciyue Shen*; Collin Schlager; Deepta Rajan; Victoria Mountain; Ilan Wapinski; Amaro Taylor-Weiner; Maryam Pouryahya; Robert Egger Self-supervised pragmatic reasoning Jennifer Hu*; Roger Levy; Noga Zaslavsky AI-Driven Predictive Analytics to Inform Nuclear Proliferation Detection in Urban Environments Anastasiya Usenko*; Joonseok Kim; Ellyn Ayton; Svitlana Volkova Measuring the Cause and Effect in Scientific Productivity: A Case Study of the ACL Community Jasmine R Eshun*; Maria Glenski; Svitlana Volkova Scalable Bayesian Network Structure Learning with Splines Charupriya Sharma*; Peter van Beek Accurate Multi-Endpoint Molecular Toxicity Predictions in Humans with Contrastive Explanations Bhanushee Sharma*; Vijil Chenthamarakshan; Amit Dhurandhar; Shiranee Pereira; James Hendler; Jonathan S Dordick; Payel Das Visual Question Answering (VQA) Models for Hypothetical Reasoning Shailaja K Sampat* Car Damage Detection and Patch-to-Patch Self-supervised Image Alignment Hanxiao Chen* Importance of Data Re-Sampling and Dimensionality Reduction in Predicting Students’ Success Eluwumi Folake Buraimoh*; Ritesh Ajoodha; Kershree Padayachee Using computer vision to measure spatial-temporal change of building conditions in neighborhoods with street view imagery Evelyn C Fitzgerald*; Tingyan Deng; Daniel Chen; Lijing Wang; Jackelyn Hwang Efficient evaluation metrics for evaluating the performance of GANs Architecture Ramat Ayobami Salami*; Sakinat O Folorunso Targeted active semi supervised learning for new customers in virtual assistants Dieu Thu Le; Verena Weber*; Melanie Bradford Graph Neural Networks for automated histologic scoring of NASH liver biopsy Maryam Pouryahya*; Jason Wang; Kenneth Leidal; Harsha Pokkalla; Dinkar Juyal; Zahil Shanis; Aryan Pedawia; Quang Huy Le; Victoria Mountain; Sara Hoffman; Murray Resnick; Michael Montalto; Andy Beck; Katy Wack; Ilan Wapinski; Oscar Carrasco-Zevallos; Amaro Taylor-Weiner Application of a Bayesian CAR Prior to Analyzing Ancient Statistical Records of the Inca Empire Anastasiya Travina* Depth without the Magic: Inductive Biases of Natural Gradient Descent Anna Mészáros*; Anna Kerekes; Ferenc Huszar We have three types of mentorship roundtables: Research roundtables, Career and Life roundtables, and Sponsor roundtables. The mentorship session is at two time slots on Fri Dec 10, 11:00 AM - 12:45 PM and 20:15 PM - 21:55 PM (UTC time in 24-hour format) in the Roundtable Rooms in WiML Gather.Town. To allow WiML attendees to gain exposure to a wide range of topics, as well as to allow mentors and mentees to connect with a variety of people, attendees (but not mentors) at the mentorship session will rotate between the discussion tables throughout the event. Specifically, there will be 3 timed opportunities to rotate between the Research, Career and Life, and Sponsorship tables. Each discussion period will last for approximately 15 minutes (during which the participants will be asked to remain at their seats for the ongoing conversation). After each 15-minute session, the WiML organizers will announce that it is time for the participants to move to a different roundtable, and all participants will have 10 minutes to explore the different rooms and decide on their discussion topic of interest. All mentors and mentees will be free to use the last 10 minutes of the roundtables session as they wish, either remaining at their discussion tables or moving between tables to meet the other WiML participants. Mentorship Roundtable I: Friday, December 10: 11:00 - 12:45 (UTC time in 24-hour format) Table 1 (Research): Reinforcement learning I (Feryal Behbahani) Table 2 (Research): Reinforcement learning II (Minhae Kwon) Table 3 (Research): Control and online learning (Katja Hofmann) Table 4 (Research): Probabilistic graphical models and Bayesian methods (Isabel Valera) Table 5 (Research): Statistical inference and estimation (Emtiyaz Khan) Table 6 (Research): Learning theory (Arthur Gretton) Table 7 (Research): AutoML (Katharina Eggensperer) Table 8 (Research): Computer Vision (Enzo Ferrante) Table 9 (Research): Robotics (Daniela Pamplona) Table 10 (Research): Fairness, accountability, and ethics in machine learning (Jessica Schrouff) Table 11 (Research): Social science applications (Alice Oh) Table 12 (Career and Life): Navigating the job search (industry) and doing research in the industry (Lavanya Tekumalla) Table 13 (Career and Life): Developing a long-term research plan (Ferenc Huszar) Table 14 (Career and Life): Navigating academia (job search and tenure application process) (Razvan Pascanu) Table 15 (Career and Life): Choosing between academia and industry (Alessandra Tosi) Table 16 (Career and Life): Seeking funding (academia edition): PhD fellowships / professorship grants (Ioana Bica) Table 17 (Career and Life): Establishing collaborations (Yarin Gal) Table 18 (Career and Life): Work-life balance (academia) (Nando de Freitas) Table 19 (Career and Life): Surviving graduate school (Truyen Tran) Table 20 (Career and Life): Work-life balance (industry) (Shane Legg) Table 21 (Career and Life): Scientific communication (Shakir Mohamed) Table 22 (Career and Life): Taking on the leadership roles (academic + industry) (Po-Ling Loh) Table A (Sponsor): Entering AI Research from other STEM fields [Deepmind] (Andy Brock, Michela Paganini) Table B (Sponsor): Careers at G-Research [G-Research] (Clara Dolfen, Jamie Watson, Olivia Bateman) Table C (Sponsor): Machine Learning at Microsoft Research [Microsoft] (Rianne van den Berg) Table D (Sponsor): Careers at NVIDIA [NVIDIA] (Monica Spehar) Mentorship Roundtable II: Friday, December 10: 20:15 - 21:55 PM (UTC time in 24-hour format) Table 1 (Research): Deep learning (Ian Goodfellow) Table 2 (Research): Reinforcement learning I (Sham Kakade) [Canceled] Table 3 (Research): Reinforcement learning II (Amy Zhang) Table 4 (Research): Optimization (Tatjana Chavdarova) Table 5 (Research): Learning theory (Karan Singh) Table 6 (Research): Natural language processing (Layla El Asri) Table 7 (Research): Data-efficient machine learning (Nicolas Le Roux) Table 8 (Research): Interpretability and explainability in machine learning (Jennifer Wortman Vaughan) Table 9 (Research): Causal inference and counterfactuals (Sarah Tan) Table 10 (Research): Robotics (Eugene Vinitsky) Table 11 (Research): Computer vision (Jennifer Hobbs) Table 12 (Research): Music applications (Pablo Samuel Castro) Table 13 (Research): Machine learning for healthcare (Adriana Romero-Soriano) Table 14 (Research): AI 4 Science (Animashree Anandkumar) Table 15 (Career and Life): Navigating the job search (industry) and doing research in industry (Timnit Gebru) Table 16 (Career and Life): Finding mentors throughout your career (Yisong Yue) Table 17 (Career and Life): Navigating academia (job search and tenure application process) (Sinead Williamson) Table 18 (Career and Life): Choosing between academia and industry (Negar Rostamzadeh) Table 19 (Career and Life): Seeking funding: negotiating compensation in industry (Samy Bengio) Table 20 (Career and Life): Establishing collaborations (Anitha Vijayakumar) Table 21 (Career and Life): Surviving graduate school (Katherine Niehaus) Table 22 (Career and Life): Building your professional brand (Chelsea Finn) Table 23 (Career and Life): Work-life balance (industry) (Wonmin Byeon) Table 24 (Career and Life): Life with kids (Sarah Poole) Table 25 (Career and Life): Scientific communication (Been Kim) Table 26 (Career and Life): Non-traditional paths to machine learning (Jennifer Wei) Table 27 (Career and Life): Doing a postdoc I (Hyeji Kim) Table 28 (Career and Life): Networking (Bethany Edmunds) Table 29 (Career and Life): Democratizing ML research: Non-traditional research methods (Jade Abbott) Table E (Sponsor): Apple Internships [Apple] (Lauren Araujo, Lauren Hannah) Table F (Sponsor): Task Oriented Dialog Research @ ASAPP [ASAPP] (Ramya Ramakrishnan, Ryan McDonald, Sravana Reddy) Table G (Sponsor): Careers in AI and ML at Capital One [Capital One] (Helen Lee-Righter, Vannia Gonzalez Macias) Table H (Sponsor): Machine Learning at D. E. Shaw Research [D. E. Shaw Research] (Jocelyn Sunseri) Table I (Sponsor): Present and Future of AI Research at Intel [Intel] (Huma Abidi, Lama Nachman) Table J (Sponsor): AI Careers at Meta (Beliz Gokkaya, Kavya Srinet, Sahar Karimi) Table K (Sponsor): Machine Learning at Microsoft Research [Microsoft] (John Langford, Nicolas Le Roux) Table L (Sponsor): From Academia to Quantitative Finance – Careers at PDT Partners [PDT Partners] (Kurt Miller, Winnie Yang) Table M (Sponsor): Meet & Greet Qualcomm AI Research [Qualcomm] (Dipika Khullar, Sangeetha Siddegowda, Shreya Kadambi) Table N (Sponsor): QuantumBlack Careers Roundtable [QuantumBlack] (Huilin Zeng, Marta Lopez, Xilin Cecilia Shi) Table O (Sponsor): Early Career Advice for Industry [Salesforce] (Shelby Heinecke, Vena Li) Table P (Sponsor): Software Story for Accelerators and Engineer Experience in a Startup World [SambaNova Systems] (Mary Jo Doherty, Weiwei Chen) Table Q (Sponsor): Self-Driven Women: Careers at Waymo [Waymo] (Drago Anguelov, Wei Chai, Chen Wu, Congcong Li, Kevin Peterson) There will also be sponsor booths in the expo room, staffed at the times below. Sponsor talks are playable by participants on-demand in the Sponsor Expo Room at Gather.Town and at the NeurIPS virtual site (NeurIPS registration required to access). Virtual Booths (in the Sponsor Expo Room at Gather.Town ) Apple (Fri Dec 10, 20:15 - 21:55 UTC) Apple at NeurIPS | Career Opportunities | Internship Opportunities | RSVP to Meet Apple | RSVP to Internship Q&A Panel Capital One (Fri Dec 10, 20:15 - 21:55 UTC) Learn more about AI and ML at Capital One | Explore Data Science Roles at Capital One DeepMind (Fri Dec 10, 11:00 - 13:45 UTC) DeepMind at NeurIPS 2021 - Schedule | DeepMind - Careers Info D. E. Shaw Research (Fri Dec 10, 13:00 - 13:45 UTC & 20:15 - 21:55 UTC) D. E. Shaw Research - Brochure G-Research (Fri Dec 10, 12:45 - 13:45 UTC) G-Research: Opportunities | G-Research: Kaggle Competition | G-Research: Spring Insight Week Meta (Fri Dec 10, 20:15 - 21:55 UTC) Meta AI at NeurIPS 2021 | Meta AI Careers Microsoft (Fri Dec 10, 20:15 - 21:55 UTC) Microsoft Research – Emerging Technology, Computer, and Software Research | Microsoft at NeurIPS 2021 - Microsoft Research NVIDIA (Fri Dec 10, 11:00 - 13:45 UTC & 20:15 - 21:55 UTC) NVIDIA @ NeurIPS2021 | NVIDIA Careers | Research at NVIDIA QuantumBlack (Fri Dec 10, 20:15 - 21:55 UTC) SambaNova Systems (Fri Dec 10, 20:15 - 21:55 UTC) ML Accelerators and Performance Sponsor Talks (in the Sponsor Expo Room at Gather.Town and at the NeurIPS virtual site ) Lizi Ottens (Apple) Machine Learning at Apple Cat Posey (Capital One) AI & ML at Capital One Jocelyn Sunseri (D. E. Shaw Research) Machine Learning Initiatives at D. E. Shaw Research Mihaela Rosca, Feryal Behbahani, and Kate Parkyn (DeepMind) Women at DeepMind - Applying for Technical Roles Daniela Massiceti (Microsoft) Advancing real-world few-shot learning with the new ORBIT dataset Anima Anandkumar (NVIDIA) Research at NVIDIA: New Core AI and Machine Learning Lab Garazi Gomez-de-Segura (QuantumBlack) ML for Engineering design Anna Bethke (Salesforce) Actionable Steps to Implement Ethics by Design Qinghua Li (SambaNova) SambaNova Systems: ML Accelerators & Performance Chen Wu (Waymo) Machine Learning for Autonomous Driving at Waymo The following participant-hosted socials will take place before, during, and after the workshop in Gather.Town in the South Garden in WiML Gather.Town. We highly encourage WiML participants to attend, to meet fellow participants in a fun and casual setting! See a description of each social here , and instructions on how to enter the area for each social here ! Pre-workshop socials Dec 9, 2:00 - 3:00 Indrapriyadarsini Sendilkkumaar, Shahrzad Mahboubi – Optimization Techniques Ayushi Sharma, Kiana Alikhademi – Applying to and Navigating PhDs Workshop socials Dec 10, 3:00 - 4:00 Jenna Hong, Devi Bhattarai – Multidisciplinary AI (Neuroscience, Social Science and Ethics) Hope Schroeder, Akshita Ramya Kamsali – Natural Language Processing and Computational Social Science Bing Zhang -- Win and Wine (Fun Social) Dec 10, 5:30 - 7:00 Mehreen Ali, Anoush Najarian – Privacy and Algorithms of Oppression Melissa Fabros -- Murder Mystery (Fun Social) Dec 10, 6:00 - 7:00 Mamatha Thota, Naina Dhingra – Computer Vision Algorithms and Applications Call for Participation WiML Workshop 2021 @ NeurIPS 16th Workshop for Women in Machine Learning Submissions are now closed, but if you would like to participate as a volunteer, poster mentor, or social host, please apply here before November 5, 2021 to be considered! The 16th Workshop for Women in Machine Learning (WiML) will be co-located with NeurIPS in December 2021 and will be held virtually. The workshop is a one-day event with invited speakers, oral and poster presentations. The event brings together members of the academic and industry research community for an opportunity to connect, exchange ideas, and learn from each other. Underrepresented groups and undergraduates interested in pursuing machine learning research are encouraged to participate. There will be virtual mentorship sessions to discuss current research trends and career choices in machine learning. While all presenters will identify as a woman, nonbinary or gender non-conforming, members of all gender identities are invited to attend. All submissions must abide by the WiML Code of Conduct . Submission page: https://cmt3.research.microsoft.com/WiML2021 Registration funding and non-author participation application: here . IMPORTANT DATES September 1, 2021 - Abstract submission opens on CMT October 5, 11:59 pm AoE - Abstract submission deadline October 20, 2021 - Notification of abstract acceptance October 20, 2021 - Application for registration fee funding and volunteering opens November 5, 2021 - Registration funding application deadline November 12, 2021 - Registration funding notification December 9-10, 2021 - WiML Workshop Day SUBMISSION INSTRUCTIONS We strongly encourage students, postdocs, and researchers in all areas of machine learning who identify as a woman, nonbinary or gender non-conforming to submit an abstract (1 page PDF) describing new, previously, or concurrently published research. We welcome abstract submissions in theory, methodology, as well as applications. While the presenting author need not be the first author of the work, we request that the presenting author be identifying as a woman, nonbinary or gender non-conforming. Submissions will be reviewed in a double-blind setting. Authors of accepted abstracts will be asked to present their work in a virtual poster session. A few authors will be selected to give spotlight or oral presentations. There are no formal proceedings. Abstracts are non-archival: they may describe completed research or work-in-progress. Please refer to the detailed Submission Instructions . REGISTRATION FEE FUNDING Registration to the NeurIPS virtual conference is required to participate in this year's WiML workshop. Registration fee funding for NeurIPS will be available for eligible WiML participants. To qualify, the participant must be a student, postdoc, or equivalent position (equivalent positions include unemployed recent grads and early career researchers from underrepresented regions or groups), and identify as a woman, nonbinary or gender non-conforming. Priority will be given to poster presenters, workshop volunteers, and first-time attendees of NeurIPS or similar conferences. Funding recipients must participate in the WiML Workshop as either a poster presenter or volunteer as outlined in the application. Funding and volunteering application form: Please check starting October 20, 2021 for the application link, when it will be made ready. The application deadline is November 5, 2021. VOLUNTEERING We are seeking volunteers to help with technical setup and virtual technology testing before and during the event, e.g., letting people into Zoom rooms, poster mentors etc. You can indicate if you can help in any way in the application form. OTHER SUBSIDIES We will also consider internet and equipment subsidies for the purpose of attending the workshop. Equipment may include headphones, microphones, funding to cover internet access, and anything else that might facilitate participation in the workshop. Please see the funding and volunteering application form for details. Questions? Check out the FAQs (https://wimlworkshop.org/faq/ ) or reach us at workshop@wimlworkshop.org PLATINUM SPONSORS GOLD SPONSORS SILVER SPONSORS BRONZE SPONSORS Committee ORGANIZERS Boyi Li General Chair Mariya I. Vasileva General Chair Linh Tran Finance and Sponsorship Chair Akiko Eriguchi Finance and Sponsorship Chair Meera Desai Logistic Chair S. Aga Lee Logistic Chair Jieyu Zhao Senior Program and Mentorship Chair Salomey Osei Senior Program and Mentorship Chair Sirisha Rambhatla Senior Program and Mentorship Chair Geeticka Chauhan Student Program and Funding Chair Nwamaka Okafor Student Program and Funding Chair ADVISORY Danielle Belgrave D&I chair Nezihe Merve Gürel WiML Board POC SUPER VOLOUNTEERS Mennatullah Siam University of Alberta Tianlin Xu London School of Economics Weiwei Zong Henry Ford Health System & University of Michigan Gloria Namanya Makerere University Sharvaree Vadgama University of Amsterdam Archana Iyer Sloan Kettering Institute Sofia Bourhim ENSIAS-Mohammed V University Silvia Pagliarini University of California, Los Angeles Liyue Shen Stanford University Maikey Khorani Salahaddin University / College of Engineering Disha Shur Purdue University Naiti Bhatt New York University Patricia Robinson Stanford University Sandareka Wickramanayake National University of Singapore Priya Bannur University of Southern California Varsha Kishore Cornell University Ria Vinod Brown University, IBM Research Niharika Vadlamudi International Institute of Information Technology, Hyderabad Bing Zhang IBM Research Mei Chen University of Waterloo Kajal Puri University of Bonn, Germany AREA CHAIRS Deepti Ghadiyaram Facebook Research Adriana Romero Facebook AI Research Amita Misra IBM Anastasiya Belyaeva MIT Angelica Aviles-Rivero University of Cambridge Ankita Shukla ASU Anna Klimovskaia Susmelj Swiss Data Science Center Anna Kruspe Technische Universität München Besmira Nushi Microsoft Research Buket Yüksel Koç University Celestine Mendler-Dünner UC Berkeley Dalin Guo UC San Diego; Twitter Inc. Erin Grant UC Berkeley Gintare Karolina Dziugaite ServiceNow Ilke Demir Intel Corporation Isabela Albuquerque Institut National de la Recherche Scientifique Kalesha Bullard Facebook AI Research Kuan-Ting Chen National Taiwan University Maria Glenski Pacific Northwest National Laboratory Mayoore Jaiswal University of Washington Mengjiao Wang Amazon Visual Search Nastraran Baradaran Citrix Systems Natalia Efremova Queen Mary University London Nesime Tatbul Intel Labs and MIT Nezihe Merve Gürel ETH Zürich Niha Beig Case Western Reserve University Nora Hollenstein University of Copenhagen Obioma Pelka University of Applied Sciences and Arts Dortmund Pallika Kanani Oracle Labs Peixian Liang University of Notre Dame Pooja Sharma BIT Sindri Rachel Cummings Georgia Tech Samira Daruki Expedia Research Sandhya Prabhakaran Moffitt Cancer Center Sandya Mannarswamy Intel India Sara Magliacane IBM Research Sergul Aydore Amazon Web Services Shinjini Ghosh MIT Shuai Zhang ETH Zürich Sima Behpour Samsung Research America Sinead Williamson UT Austin Spandana Gella Amazon AI Subarna Tripathi Intel Labs Surangika Ranathunga University of Moratuwa Swetasudha Panda Oracle Labs Tania Lorido-Botran Independent Researcher Xenia Miscouridou Imperial College London Xi Rao ETH Zürich Xiao Zhang T-Mobile Xun Tang Yelp Yao Qin University of California, San Diego FAQs Do you have a list of members? How can I join WiML? WiML doesn’t have “members” per se, any women working in machine learning can be part of the WiML network. We have a mailing list for anyone to post announcements of interest to the WiML network and an opt-in, necessarily incomplete directory of women working in machine learning . How can I join the WiML mailing list? Join the mailing list directly here . What kind of events do you organize? Our flagship event is the annual WiML Workshop, typically co-located with NeurIPS, a machine learning conference. We also organize an “un-workshop” at ICML, as well as small events (e.g. lunches and receptions) at other machine learning conferences, such as CoRL, COLT, etc. Check out our events page for up-to-date listings of events. Do you have local meetups? No, but check out WiMLDS (website, Twitter), another organization that supports women in machine learning by organizing local meetups. How do I reach the WiML network? Use our mailing list . How can I sponsor WiML? Thank you for your interest in sponsoring WiML! See this page for more information. I am looking for an invited speaker / panelist / area chair / program committee member etc. Can WiML help me? Use our directory of women in machine learning or post this opportunity to our mailing list . I want to circulate a job posting. Can WiML help me? Post directly to our mailing list . How can I support WiML? You can: Post interesting opportunities and job postings to our mailing list . Use our directory of women in machine learning to find invited speakers, panelists, area chairs, program committee members, etc, or post these opportunities to our mailing list . Sponsor us. See this page for more information. Volunteer at one of our events. Check out our events page for up-to-date listings of events. Apply to be an area chair or reviewer at WiML Workshop (see this year’s workshop website for info). Take pictures at our events and share with us (tag @wimlworkshop on Twitter). If you see us mentioned in the media, send us a link at info@wimlworkshop.org . And many others! How did WiML start? What's the founding story? Hanna Wallach, Jennifer Wortman Vaughan, Lisa Wainer, and Angela Yu shared a room at NIPS 2005. Late one night, they talked about how exciting it was that there were FOUR female students at NIPS that year. They tried to list all the women in machine learning they know of and got to 10, then started talking about creating a meeting or gathering for all these women and perhaps others that they didn’t know about. Jenn, Lisa, and Hanna put together a proposal for a session at the 2006 Grace Hopper Celebration of Women in Computing that would feature talks and posters by female researchers and students in machine learning. The 1st WiML workshop was co-located with the 2006 Grace Hopper Celeberation. In 2008, WiML Workshop moved to NIPS (renamed NeurIPS in 2018) and there has been a WiML Workshop at NeurIPS every year since. In 2020, WiML introduced an “un-workshop” at ICML based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. Read more WiML history here! I am a man. Can I attend WiML? Yes. Allies are welcome to attend! Note, however, that all speakers and poster presenters will primarily identify as women, nonbinary, or gender-nonconforming, as our goal is to promote them and their work within the machine learning community. What are the mentorship roundtables? Each table seats 8-10 people (including mentors), with two mentors leading the discussion on a particular topic at each table. WiML attendees rotate between tables every 15-20 minutes. This allows attendees to gain exposure to different topics, and mentors to meet a large number of WiML attendees. Is WiML an archival venue? No, WiML is a non-archival venue. This means that, if your contribution is accepted, we will not be asking you to submit a camera-ready version of it, nor will we publish it anywhere (neither online nor in proceedings of any sort). We will only make the title and authors’ names available in the program book. I have a question that isn't answered here. How do I reach you? We receive a lot of email. Help us help you by reaching out through the appropriate channels. Job posting, announcement, CFP, etc: Post directly to WiML mailing list . Have event pictures to share: post on Twitter and tag @wimlworkshop Workshop enquiries: workshop@wimlworkshop.org If you are a company interested in sponsoring WiML: sponsorship@wimlworkshop.org Any other enquiries: info@wimlworkshop.org If you email us, don’t cc multiple email addresses — this saves us time routing your email to one mailbox, and reduces the chances of your email getting lost. Thank you in advance! Back To Top

3rd Women in Machine Learning Un-Workshop, ICML 2022 The 3rd WiML Un-Workshop is co-located with ICML on Monday, July 18th, 2022. Speakers Logistics Breakout Sessions Program Call for Participation Committee FAQ Code Of Conduct Machine learning is one of the fastest growing areas of computer science research. Search engines, text mining, social media analytics, face recognition, DNA sequence analysis, speech and handwriting recognition, healthcare analytics are just some of the applications in which machine learning is routinely used. In spite of the wide reach of machine learning and the variety of theory and applications, it covers, the percentage of female researchers is lower than in many other areas of computer science. Most women working in machine learning rarely get the chance to interact with other female researchers, making it easy to feel isolated and hard to find role models. The annual Women in Machine Learning Workshop is the flagship event of Women in Machine Learning, primarily intended to foster active participant engagement in the program. This technical workshop gives female faculty, research scientists, and graduate students in the machine learning community an opportunity to meet, network and exchange ideas, participate in career-focused panel discussions with senior women in industry and academia and learn from each other. Underrepresented minorities and undergraduates interested in machine learning research are encouraged to attend. We welcome all genders; however, any formal presentations, i.e. talks and posters, are given by women. We strive to create an atmosphere in which participants feel comfortable to engage in technical and career-related conversations. Now in its 3th year, the 2022 un-workshop is co-located with ICML . Besides this un-workshop and annual workshop which is co-located with NeurIPS, Women in Machine Learning also organizes events such as lunch or social at the AISTATS or AAAI conferences, maintains a public directory of women active in ML, profiles the research of women in ML, and maintains a list of resources for women working in ML. Invited Speakers Emma Brunskill Emma Brunskill is an associate professor in the Computer Science Department at Stanford University. Her goal is to create AI systems that learn from few samples to robustly make good decisions, motivated by our applications to healthcare and education. Her work has been honored by early faculty career awards (National Science Foundation, Office of Naval Research, Microsoft Research) received several best research paper nominations (CHI, EDMx3) and awards (UAI, RLDM, ITS). Celestine Mendler-Dünner Celestine Mendler-Dünner is a research group lead at the Max Planck Institute for Intelligent Systems in Tübingen. Her research focuses on the role of society in the study of computation, taking into account actions and reactions of individuals when analyzing and designing algorithmic systems. Prior to joining MPI-IS Celestine was a SNSF postdoctoral fellow at UC Berkeley, and a predoctoral researcher at IBM Research Zurich. She obtained her PhD from ETH Zurich where she was awarded the ETH medal and the Fritz Kutter prize for the academic as well as the industrial impact of her research. Yixin Wang Yixin Wang is an LSA Collegiate Fellow and an assistant professor of statistics at the University of Michigan. She works in the fields of Bayesian statistics, machine learning, and causal inference. Her research has received several awards, including the INFORMS data mining best paper award, Blackwell-Rosenbluth Award from the junior section of ISBA, student paper awards from ASA Biometrics Section and Bayesian Statistics Section, and the ICSA conference young researcher award. Location This workshop will be hybrid, co-located with ICML at the Baltimore Convention Center , Baltimore, Maryland USA. Type of registration required to attend Any type of in-person registration (tutorial / workshop / conference / all) grants you in-person access to the un-workshop. Also, an in-person registration includes access to the virtual one. Breakout Sessions Breakout Sessions During the day of the WiML Un-Workshop @ ICML 2022 there will be three different Breakout Sessions. We list the sessions, topics, and leaders. BreakoutGhoshehBreakout Session #1 (9.10AM - 10.10AM) IN-PERSON Breakout Sessions Machine learning real-time applications in health. Leader: Dania Humaidan, Co-leader: Cansu Sen. VIRTUAL Breakout Sessions Deep Generative Models for Electronic Health Records. Leader: Ghadeer Ghosheh, Co-leader: Tingting Zhu. Affective Computing: A Computational Perspective. Leader: Shreya Ghosh, Co-lead: Garima Sharma. Introducing geometry awareness in deep networks. Leader: Ankita Shukla. Breakout Session #2 (11.05AM - 12.05AM) IN-PERSON Breakout Sessions Challenges and opportunities in certified auditing of ML models. Leader: Chhavi Yadav. Robustness of Deep Learning Models to Distribution Shift. Leader: Polina Kirichenko, Co-leads: Shiori Sagawa, Sanae Lofti. VIRTUAL Breakout Sessions Knowledge Distillation through the lense of the capacity gap problem. Leader: Ibtihel Amara, Co-lead: Samrudhdhi Rangrej, Zahra Vaseqi. Improving AI Education. Leader: Mary Smart, Co-lead: Stefania Druga. Statistical Inference & Applications to Machine Learning. Leader: Lilian Wong, Co-lead: Po-ling Loh. Breakout Session #3 (15.25 - 16.25) IN-PERSON Breakout Sessions Robustness of Machine Learning. Leader: Yao Qin Towards efficient and robust deep learning training. Leader: Wenhan Xia. VIRTUAL Breakout Sessions Machine Learning for Physical Sciences. Leader: Taoli Cheng. Limitations of explainable/interpretable AI: frontiers and boundaries for future advancement. Leader: Haoyu Du, Co-lead: Peiyuan Zhou, Annie Lee, Rainah Khan. Detection of Unseen Classes of different Domains using Computer Vision. Leader: Asra Aslam. PROGRAM PANELISTS IN-PERSON MENTORS VIRTUAL MENTORS POSTERS The program follows the following color scheme: talks , breakout sessions , poster sessions , mentoring sessions , program break , sponsor talks , panel discussion . All invited talk titles, and invited speaker/mentor/panelist names are *clickable*. The majority of the program will be streamed and occur synchronously in-person and virtually, except if marked as in-person/virtual only. You can find the zoom links and livestream on the WiML workshop page of the ICML website . 08:30 Introduction & Opening Remarks , Vinitra Swamy all-day Virtual Sponsor Booths , [DeepMind, D.E. Shaw Research, Home Depot, Microsoft Research] all-day In-Person Sponsor Booths , [DeepMind, Google, QuantumBlack] 08:45 Desiderata for Representation Learning: A Causal Perspective , Yixin Wang [Invited Talk] Abstract: Representation learning constructs low-dimensional representations to summarize essential features of high-dimensional data like images and texts. Ideally, such a representation should efficiently capture non-spurious features of the data. It shall also be disentangled so that we can interpret what feature each of its dimensions capture. However, these desiderata are often intuitively defined and challenging to quantify or enforce. In this talk, we take on a causal perspective of representation learning. We show how desiderata of representation learning can be formalized using counterfactual notions, enabling metrics and algorithms that target efficient, non-spurious, and disentangled representations of data. We discuss the theoretical underpinnings of the algorithm and illustrate its empirical performance in both supervised and unsupervised representation learning. Joint work with Michael Jordan . 09:10 Breakout session [in-person only] Machine learning real-time applications in health (Leaders: Dania Humaidan, Cansu Sen) [hybrid] Introducing geometry awareness in deep networks (Leader: Ankita Shukla) [hybrid] Affective Computing: A Computational Perspective (Leaders: Shreya Ghosh, Garima Sharma) [hybrid] Deep Generative Models for Electronic Health Records (Leaders: Ghadeer Ghosheh) 10:10 Poster Session 10:40 Emma Brunskill [Invited Talk] 11:05 Breakout session [in-person only] Challenges and opportunities in certified auditing of ML models (Leader: Chhavi Yadav) [in-person only] Robustness of Deep Learning Models to Distribution Shift (Leaders: Polina Kirichenko, Shiori Sagawa) [hybrid] Knowledge Distillation through the Lens of the Capacity Gap Problem (Leaders: Ibtihel Amara, Samrudhdhi Rangrej, Zahra Vaseqi) [hybrid] Improving AI Education (Leaders: Mary Smart, Stefania Druga) [hybrid] Statistical Inference & Applications to Machine Learning (Leaders: Lilian Wong, Po-ling Loh) 12:05 Mentoring Roundtables [in-person only] /// Mentoring Panel [virtual only] Table 1: Choosing between academia and industry Amy Zhang & Lauren Gardiner Mentors: Jigyasa Grover , Ciara Pike-Burke, Nika Haghtalab, Po-Ling Loh, Hermina Petric Maretic Table 2: Finding mentors and taking on mentorship roles throughout your career / Celestine Mendler-Dünner & Cyril Zhang Moderator: Sinead Williamson Table 3: Establishing and maintaining collaborations Surbhi Goel & Max Simchowitz Table 4: Work-life Balance Ioana Bica & Kishore Kumar 13:05 Lunch Break, joint with NewInML [in-person only] /// Virtual Sponsor Booths [virtual only] 14:40 Harnessing the power of Hybrid Intelligence, Maria Olivia Lihn [QuantumBlack Sponsor Talk] 14:55 Building embodied agents that can learn from their environments and humans, Kavya Srinet [Meta Platforms Sponsor Talk] 15:10 Machine Learning at Apple, Tatiana Likhomanenko [Apple Sponsor Talk] 15:25 Breakout session [in-person only] Robustness of Machine Learning (Leader: Yao Qin) [in-person only] Distributionally robust Reinforcement Learning (Leaders: Laixi Shi, Mengdi Xu) [hybrid] Machine Learning for Physical Sciences (Leader: Taoli Cheng) [hybrid] Limitations of explainable/interpretable AI: frontiers and boundaries for future advancement (Leaders: Haoyu Du, Peiyuan Zhou, Annie Lee, Rainah Khan) [hybrid] Detection of Unseen Classes of different Domains using Computer Vision (Leader: Asra Aslam) 16:30 Poster Session, joint with LXAI 17:00 Social dynamics in prediction, Celestine Mendler-Dünner [Invited Talk] Abstract: Algorithmic predictions inform consequential decisions, incentivize strategic actions, and motivate precautionary measures. As such, predictions used in societal systems not only describe the world they aim to predict, but they have the power to change it; a prevalent phenomenon often neglected in theories and practices of machine learning. In this talk, I will introduce a risk minimization framework, called performative prediction, that conceptualizes this phenomenon by allowing the predictive model to influence the distribution over future data. This problem formulation elucidates different algorithmic solution concepts, optimization challenges, and offers a new perspective on prediction. In particular, I will discuss how performative prediction allows us to articulate the difference between learning from a population and steering a population through predictions, facilitating an emerging discourse on the topic of power of predictive systems in digital economies. 17:25 Best Practices for Research: Increasing Efficiency and Research Impact, and Navigating Hybrid Collaborations [Panel] Panelists: Amy Zhang , Surbhi Goel , Agni Kumar Moderator: Ioana Bica 18:25 Closing Remarks, Tatjana Chavdarova Note: Please navigate the 'Program' menu in the slidebar at the top to find more details about speakers, panelist and mentors. Surbhi Goel Surbhi Goel is currently a postdoctoral researcher at Microsoft Research NYC. In Spring 2023, she will be starting as the Magerman Term Assistant Professor of Computer and Information Science at University of Pennsylvania. Prior to this, she received her Ph.D. from the Department of Computer Science at the University of Texas at Austin where she was advised by Adam Klivans. Her work lies at the intersection of machine learning and theoretical computer science, with a focus on developing the statistical and computational foundations of modern machine learning paradigms. Among other honors, she is a recipient of UT Austin's Bert Kay Dissertation award, a J.P. Morgan AI PhD fellowship, and a Simons-Berkeley research fellowship. She has been recognized as a Rising Star in ML by University of Maryland and in EECS by UIUC. She is actively involved in service and outreach through her role as the co-founder of Learning Theory Alliance (LeT-All), a community building and mentorship initiative for the learning theory community. Amy Zhang Amy is a postdoctoral scholar at UC Berkeley and a research scientist at Facebook AI Research, and is starting as an assistant professor at UT Austin in the ECE department in Spring 2023. She works on state abstractions, model-based reinforcement learning, representation learning, and generalization in RL. She did her PhD at McGill University and Mila - Quebec AI Institute, co-supervised by Joelle Pineau and Doina Precup. She also has an M.Eng. in EECS and dual B.Sci. degrees in Mathematics and EECS from MIT. Agni Kumar Agni is an Applied Research Scientist on Apple’s Health AI team. She studied at MIT, graduating with an M.Eng. in Machine Learning and B.S. degrees in Mathematics and Computer Science. Her thesis on modeling the spread of healthcare-associated infections led to joining projects at Apple with applied health focuses, specifically on understanding cognitive decline from device usage data and discerning respiratory rate from wearable microphone audio. She has published hierarchical reinforcement learning research and predictive modeling work in conferences and journals, including CHIL, EMBC, PLOS Computational Biology, and Telehealth and Medicine Today. She was a workshop organizer for ICML’s first “Computational Approaches to Mental Health” workshop in 2021. She has also volunteered at WiML workshops and served as a reviewer for NeurIPS. For joy, Agni leads an Apple-wide global diversity network about encouraging mindfulness to find peace each day. Ioana Bica (Moderator) Ioana is a rising fifth-year PhD student at the University of Oxford and at the Alan Turing Institute, advised by Prof. Mihaela van der Schaar. Her PhD research focuses on building machine learning methods for improving and understanding decision making. To achieve this, she have worked on developing causal inference methods capable of estimating the individualized effect of interventions (e.g. actions or treatments) from observational data. Her research experience also includes an internship at DeepMind where she has been working with Jovana Mitrović on self-supervised learning and causality with the aim of learning better representations for objects in images. Prior to her PhD, she completed a Bachelor’s degree and a Master’s degree in Computer Science at the University of Cambridge where she worked with Prof. Pietro Liò on multi-modal data integration and unsupervised learning for genomics data. During this time, she has also interned at Google four times. Amy Zhang Amy is a postdoctoral scholar at UC Berkeley and a research scientist at Facebook AI Research, and is starting as an assistant professor at UT Austin in the ECE department in Spring 2023. She works on state abstractions, model-based reinforcement learning, representation learning, and generalization in RL Celestine Mendler-Dünner Celestine is a research group lead at the Max Planck Institute for Intelligent Systems in Tübingen. Her research focuses on the role of society in the study of computation, taking into account actions and reactions of individuals when analyzing and designing algorithmic systems. Surbhi Goel Surbhi is currently a postdoctoral researcher at Microsoft Research NYC. In Spring 2023, she will be starting as the Magerman Term Assistant Professor of Computer and Information Science at University of Pennsylvania. Prior to this, she received her Ph.D. from the Department of Computer Science at the University of Texas at Austin where she was advised by Adam Klivans. Her work lies at the intersection of machine learning and theoretical computer science, with a focus on developing the statistical and computational foundations of modern machine learning paradigms. Ioana Bica Ioana is a rising fifth-year PhD student at the University of Oxford and at the Alan Turing Institute, advised by Prof. Mihaela van der Schaar. Her research focuses on building machine learning methods for improving and understanding decision making. Lauren Gardiner Lauren is Senior Applied Research Scientist in the Health AI team at Apple. Cyril Zhang Cyril is a Senior Researcher at Microsoft Research NYC. His research interests include sequential prediction and decision-making, the theory and practice of optimization (especially in deep learning), and the synthesis of these topics (especially in language models). Max Simchowitz Max is a postdoc in Russ Tedrake's group at MIT. His recent work has focused on the theoretical foundations of online control and reinforcement learning, with past research ranging broadly across topics in adaptive sampling, multi-arm bandits, complexity of convex and non-convex optimization, and fairness in machine learning. He is currently interested in developing rigorous, theoretical guarantees for nonlinear control, wherever possible. Kishore Kumar Kumar is a Data Science and Analytics Lead at Amazon Prime video. He strives to solve complex business problems using advanced Machine Learning Algorithms, and has 10+ years of overall experience across multiple sectors. Nika Haghtalab Nika is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. She works broadly on the theoretical aspects of machine learning and algorithmic economics. Ciara Pike-Burke Ciara is a Lecturer in Statistics in the Department of Mathematics at Imperial College London. Her research is in the field of statistical machine learning, particularly interested in sequential decision making problems. Hermina Petric Maretic Hermina is an Applied Scientist at Amazon working on time series forecasting. Her research interests include optimal transport, graphical models, network inference and interpretability. Po-Ling Loh Po-ling is a Lecturer in the Statistical Laboratory in the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge. Her interests include high-dimensional statistics, optimization, network inference, and statistical applications to medical imaging and epidemiology. Jigyasa Grover Jigyasa is a Senior Machine Learning Engineer at Twitter working in the Online Ads Prediction & Ranking domain, where she is spearheading a variety of projects spanning ML model development, user tracking transparency remediations, and monetizing new Twitter products. Sinead Williamson (Moderator) Sinead is an Assistant Professor of Statistics at the University of Texas at Austin, in the Department of Statistics and Data Science. Her research interests include network analysis, scalable inference methods, and bayesian nonparametrics. Self-Similarity Priors: Neural Collages as Differentiable Fractal Representation s Michael Poli, Winnie Xu, Stefano Massaroli, Chenlin Meng, Kuno Kim, Stefano Ermon [poster] Interpretable Adversarial Attacks using Frank Wolfe Tooba Imtiaz1, Morgan Kohler, Jared Miller, Octavia Camps, Mario Sznaier, Jennifer Dy [poster] Robust task-specific adaption of drug-target interaction models Emma Svensson, Pieter-jan Hoedt, Sepp Hochroiter, Gunter Klambauer [poster] Multi-modal Contrastive Learning with CLOOB Andreas Fürst, Elisabeth Rumetshofer, Johannes Lehner, Viet Tran, Fei Tang, Hubert Ramsauer, David Kreil, Michael Kopp, Günter Klambauer, Angela Bitto-Nemling, Sepp Hochreiter [poster] Mimicking Iterative Learning with Modern Hopfield Networks for Tabular Data Bernhard Schäfl, Lukas Gruber, Angela Bitto-Nemling, Sepp Hochreiter [poster] A Recurrent Neural Network Model of Travel Direction in Humans Lilian Cheng, Elizabeth R. Chrastil, Jeffrey Krichmar [poster] Automated Deep Lineage Tree Analysis Using Deep Learning with a Bayesian Single Cell Tracking Approach Kristina Ulicna, Giulia Vallardi, Guillaume Charras, Alan R. Lowe [poster] Prostate Cancer Malignancy Detection and Localization From MpMRI Using Auto-Deep Learning: One Step Closer to Clinical Utilization W. ZONG, E. CARVER, S. ZHU , E. SCHAFF, D. CHAPMAN, J. LEE, I. CHETTY, N. WEN [poster] Explaining Structure Activity Relationships Using Locally Faithful Surrogate Models Heta A. Gandhi, Andrew D. White [poster] Affects of Remote Learning on Academic Performance of High School Students Garima Giri, Robert M. Scott, Snigdha Chaturvedi [poster] Fourier-Based Strategies to Explore Ethnic Feature Generation during Visible-to-Thermal Facial Translation (Work-in-progress) Catherine Ordun, Edward Raff, Sanjay Purushotham [poster] Cross-modal contrastive learning of microscopy image and structure-based representations of molecules Ana Sanchez-Fernandez, Elisabeth Rumetshofer, Sepp Hochreiter, Günter Klambauer [poster] CNN-based Emotion Recognition from Multimodal Peripheral Physiological Signals Sowmya Vijayakumar, Ronan Flynn, Peter Corcoran, Niall Murray [poster] Cancer Health Disparity with BERTopic and PyCaret Evaluation Mary Adewunmi, Saksham Kumar Sharma, Nistha Sharma, N Sudha Sharmaa, Bayangmbe Mounmo [poster] Bayesian Optimisation for Active Monitoring of Air Pollution Sigrid Passano Hellan, Christopher G. Lucas and Nigel H. Goddard [poster] Detecting Seen/Unseen Objects with Reducing Response Time for Multimedia Event Processing Asra Aslam [poster] Automated Adaptive Design in Real Time Desi R. Ivanova, Adam Foster, Steven Kleinegesse, Michael U. Gutmann, Tom Rainforth [poster] [Talk] Early Identification of Tuta absoluta in Tomato Plants Using Deep Learning Lilian Mkonyi, Denis Rubanga, Baraka Maiseli, Dina Machuve [poster] Fast and Accurate Method for the Segmentation of Diabetic Foot UlcerImages Rehema Mwawado,Mussa Dida,Baraka Maiseli [poster] Deep Kernel Learning with Personalized Multi-task Gaussian Processes for Longitudinal Prediction in Alzheimer’s Disease Vasiliki Tassopoulou, Fanyang Yu, Christos Davatzikos [poster] Learning to Solve PDE-constrained Inverse Problems with Graph Networks Qingqing Zhao, David Lindell, Gordon Wetzstein [poster] [Talk] Not All Poisons are Created Equal: Robust Training against Data Poisoning Yu Yang, Tian Yu Liu, Baharan Mirzasoleiman [poster] Call for Participation WiML 3rd Un-Workshop @ ICML 2022 [submissions are now closed] The Women in Machine Learning will be organizing the third un-workshop at ICML 2022. The un-workshop is based on the concept of an un-conference, a form of discussion on a pre-selected topic that is primarily driven by participants. Different from the traditional workshop format, the un-workshop’s main focus is topical breakout sessions with short invited talks and casual, informal poster presentations. This is an event format to encourage more participant interaction and we are excited to be able to explore this format in-person for the first time! The overall goal of the un-workshop is to advance research through collaboration and increased interaction among participants from diverse backgrounds. Students, postdocs and researchers in all areas of Machine Learning who are woman or non-binary are encouraged to submit a one-page proposal to lead a breakout session on a certain research topic. There are many ways to participate, see below! IMPORTANT DATES May 27th, 2022 -- Application Form opens June 17th 19th, 2022 -- Deadline (Anywhere on Earth ) to apply for a breakout session, poster, registration fee funding, facilitating or volunteering June 20th, 2022 -- Notification of acceptance for all of the above (midnight Anywhere on Earth ) July 18th, 2022 -- WiML Un-Workshop Day Various ways of participating in WiML un-workshop Lead a breakout session: submit a proposal to lead a breakout session on a certain research topic. Facilitate a breakout session: assist breakout session leaders by taking notes and encouraging participant interactions and taking attendance. Present a poster: present a poster in a casual, informal setting. Volunteer: help with technical setup and in-event needs. Attend: participate in breakout session discussions. 1. Breakout session proposals: A breakout session is a 1-hour free-form discussion overseen by 1-3 leaders and with assistance from 1-2 facilitators to take notes and encourage participant interactions. We strongly encourage students, postdocs, and researchers who are women or nonbinary in all areas of machine learning to submit a proposal to lead a topical breakout session. A complete proposal consists of a 1 page blind PDF (example here) and the names and bios of leaders submitted separately in the application form . We strongly recommend having at least 2 leaders, with a diverse set of leaders preferred (see selection criteria below). The names of facilitators can also be provided if known at submission time. Otherwise, the organizers will match facilitators to breakout sessions. WiML registration fee funding is prioritized for accepted breakout session leaders who fulfill certain eligibility criteria (see details below) and do not have any other sources of funding. Only one proposal submission per leader is allowed. If there are multiple leaders, only one leader needs to submit the proposal. There are no proceedings. Guidelines for and roles of leaders: Breakout session leaders must be women or nonbinary Point-out key characteristics of your topic and make connections with other topics. Describe the key challenges in this research area on a high-level. Describe the key approaches on a high-level to provide intuition. Highlight possible points of discussion/goals to achieve during the session. Use graphics/imagery and materials e.g. slides as needed Encourage inclusive (rather than unilateral) discussions Leaders should anticipate a small additional time commitment before the un-workshop to receive briefing/training and a possible dry run. Submission instructions for breakout sessions: Proposals must be no more than 1 page (including any references, tables, and figures) submitted as a PDF. Main body text must be minimum 11 point font size and page margins must be minimum 0.75 inches (all sides). Your proposal should stand alone, without linking to a longer paper or supplement. You should provide a brief description of the topics you’d like to discuss, any relevant references, a plan for how you would organize the time (1 hour) allocated for a session, as well as some ideas on how you would encourage discussion and participant interaction during the session. The PDF must not include identifying information, as it will be reviewed blind. In particular, the PDF should not contain information of the leaders or facilitators. Instead, submit their information in the application form . Selection criteria for breakout sessions: The degree to which it is expected that participants will find the topic interesting and valuable. Diversity of leaders and facilitators, including diversity of experience/seniority, affiliation, race, viewpoint and thinking regarding the topic, etc. Plans for encouraging discussion and participant interaction during the session. 2. Facilitators: If you are interested in facilitating a breakout session but have not yet connected with anyone submitting a breakout session proposal, you can indicate your interest in the application form . The role of facilitators is take notes and encourage participant interactions. Organizers will match selected facilitators to breakout sessions. Facilitators should anticipate a small additional time commitment before the un-workshop to receive briefing/training and a possible dry run. Also note that facilitators can be of any gender. 3. Posters: If you wish to present a poster, submit EITHER a short abstract (max 1500 characters) OR a PDF of the poster (only if you have it already). The poster may describe new, previously, concurrently published, or work-in-progress research. Posters in theory, methods, and applications are welcome. Accepted posters will be presented in a casual, informal setting. This setting is very different from formal poster sessions, e.g. at WiML Workshop at NeurIPS. While the exact presentation format is still being determined, we expect to be able to provide spots for everyone to display a physical poster. There are no oral or spotlight presentations, but you will be invited to submit a 5-10 minute video presentation uploaded to a video streaming service. Note that there are no proceedings. Submission instructions for posters: Submitted materials may contain identifying information, as posters for this un-workshop are not reviewed blind. Your submission should stand alone, without linking to a longer paper or supplement. You should convey motivation and give some technical details of the approach used. While we acknowledge that space is limited, some experimental results are likely to improve reviewers’ opinions of your poster. The poster presenter be woman or nonbinary; other authors can be of any gender. The poster presenter does not need to be the first author of the work. Only one poster submission per presenter is allowed. If your poster is not prepared yet, you can submit a one-page abstract, examples of accepted abstracts from previous years can be found here , and advice on writing a one-page abstract can be found here . 4. Volunteering: We are seeking volunteers to help with technical setup and virtual technology testing before the event, as well as help during the event, e.g. letting people into Zoom rooms, etc. We may also need emergency reviewers for breakout session proposals. You can indicate if you can help in any way in corresponding section of the application form . Note: We also encourage you to apply for ICML volunteer and funding opportunities, which are separate and independent of WiML funding. Check the ICML website directly for details. 5. Participation instructions: To participate in ANY of the above roles and/or apply for registration fee funding, please fill in the application form by June 17, 2022. Selected breakout session leaders, facilitators, poster presenters, volunteers, and funding recipients will be notified individually by the dates mentioned above. If you only wish to attend, we still recommend you fill in this form to provide your timezone and topic preferences. All participants are required to abide by the WiML Code of Conduct. 6. Registration fee funding: To apply for funding, you should: (i) be a woman or nonbinary; (ii) be a student, postdoc, or have an equivalent position (equivalent positions include unemployed recent grads and early career researchers from underrepresented geographical regions); (iii) participate in at least one breakout session as a leader, facilitator, or attendee. Due to limited funding, we may not be able to support everyone eligible; however, we hope to support as many eligible applicants as possible. Accepted breakout session leaders who fulfill the above eligibility criteria and do not have any other sources of funding will be prioritized for WiML funding. Other participants are also encouraged to apply. Priority will be given to individuals from underrepresented regions or groups, first-time attendees of ICML or similar conferences, and individuals who would benefit the most from this funding. Further questions? Check out the FAQs or reach us at workshop@wimlworkshop.org PLATINUM SPONSORS Committee ORGANIZERS Paula Gradu General Chair Vinitra Swamy Program Chair Giulia Clerici Breakout Program and Logistics Co-Chair Mozhgan Saeidi Breakout Program and Logistics Co-Chair Noor Sajid Student Program and Volunteers Chair Yina Lin Networking and Mentorship Chair Shweta Khushu Finance and Sponsorship Chair Deeksha Shama Social Event Chair ADVISORY Danielle Belgrave D&I chair Tatjana Chavdarova WiML Board POC SUPER VOLOUNTEERS Archana Vaidheeswaran Women who code FAQs Do you have a list of members? How can I join WiML? WiML doesn’t have “members” per se, any women working in machine learning can be part of the WiML network. We have a mailing list for anyone to post announcements of interest to the WiML network and an opt-in, necessarily incomplete directory of women working in machine learning . How can I join the WiML mailing list? Join the mailing list directly here . What kind of events do you organize? Our flagship event is the annual WiML Workshop, typically co-located with NeurIPS, a machine learning conference. We also organize an “un-workshop” at ICML, as well as small events (e.g. lunches and receptions) at other machine learning conferences, such as CoRL, COLT, etc. Check out our events page for up-to-date listings of events. Do you have local meetups? No, but check out WiMLDS (website, Twitter), another organization that supports women in machine learning by organizing local meetups. How do I reach the WiML network? Use our mailing list . How can I sponsor WiML? Thank you for your interest in sponsoring WiML! See this page for more information. I am looking for an invited speaker / panelist / area chair / program committee member etc. Can WiML help me? Use our directory of women in machine learning or post this opportunity to our mailing list . I want to circulate a job posting. Can WiML help me? Post directly to our mailing list . How can I support WiML? You can: Post interesting opportunities and job postings to our mailing list . Use our directory of women in machine learning to find invited speakers, panelists, area chairs, program committee members, etc, or post these opportunities to our mailing list . Sponsor us. See this page for more information. Volunteer at one of our events. Check out our events page for up-to-date listings of events. Apply to be an area chair or reviewer at WiML Workshop (see this year’s workshop website for info). Take pictures at our events and share with us (tag @wimlworkshop on Twitter). If you see us mentioned in the media, send us a link at info@wimlworkshop.org . And many others! How did WiML start? What's the founding story? Hanna Wallach, Jennifer Wortman Vaughan, Lisa Wainer, and Angela Yu shared a room at NIPS 2005. Late one night, they talked about how exciting it was that there were FOUR female students at NIPS that year. They tried to list all the women in machine learning they know of and got to 10, then started talking about creating a meeting or gathering for all these women and perhaps others that they didn’t know about. Jenn, Lisa, and Hanna put together a proposal for a session at the 2006 Grace Hopper Celebration of Women in Computing that would feature talks and posters by female researchers and students in machine learning. The 1st WiML workshop was co-located with the 2006 Grace Hopper Celeberation. In 2008, WiML Workshop moved to NIPS (renamed NeurIPS in 2018) and there has been a WiML Workshop at NeurIPS every year since. In 2020, WiML introduced an “un-workshop” at ICML based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. Read more WiML history here ! What are the mentorship roundtables? Each table seats 8-10 people (including mentors), with two mentors leading the discussion on a particular topic at each table. WiML attendees rotate between tables every 15-20 minutes. This allows attendees to gain exposure to different topics, and mentors to meet a large number of WiML attendees. Is WiML an archival venue? No, WiML is a non-archival venue. This means that, if your contribution is accepted, we will not be asking you to submit a camera-ready version of it, nor will we publish it anywhere (neither online nor in proceedings of any sort). We will only make the title and authors’ names available in the program book. I have a question that isn't answered here. How do I reach you? We receive a lot of email. Help us help you by reaching out through the appropriate channels. Job posting, announcement, CFP, etc: Post directly to WiML mailing list . Have event pictures to share: post on Twitter and tag @wimlworkshop Workshop enquiries: workshop@wimlworkshop.org If you are a company interested in sponsoring WiML: sponsorship@wimlworkshop.org Any other enquiries: info@wimlworkshop.org If you email us, don’t cc multiple email addresses — this saves us time routing your email to one mailbox, and reduces the chances of your email getting lost. Thank you in advance! Back To Top

MAILING LIST Please use our mailing list to post job postings, announcements, calls for participation, etc. DIRECTORY & PROFILES OF WOMEN IN MACHINE LEARNING Please use our directory of women working in machine learning to find invited speakers, area chairs, conference committee members, etc. Also check out our profiles on women in machine learning . OTHER CONFERENCES & WORKSHOPS Grace Hopper Celebration Women in Data Science Conference Rising Stars EECS Workshop CRA-W Grad Cohort Workshop NextProf workshop CODE OF CONDUCT & CONFERENCE GUIDELINES NAACL Conference Anti-Harassment Policy SIGPLAN Conference Code of Conduct Policy CRA-W guidelines for running an inclusive conference LOCAL MEETUPS WiML does not organize local meetups. However, WiMLDS, another organization does! Check them out at their website or Twitter . FUNDING OPPORTUNITIES Google Travel and Conference Grants L'Oreal USA For Women in Science Fellowship GENDER BIAS Avoiding gender bias in reference writing Gender bias calculator CONFERENCE TIPS “Nine things I wish I had known the first time I came to NIPS ” by Jennifer Wortman Vaughan, WiML co-founder AWARD OPPORTUNITIES CRA-W awards ACM Athena Lecturer award OTHER DIVERSITY GROUPS Women in Computer Vision Widening NLP Black in AI LatinX in AI Queer in AI

ABOUT WiML WORKSHOPS Opportunity to meet, exchange ideas and learn from each other The annual Women in Machine Learning (WiML) Workshop, co-located with NeurIPS, is our flagship event. This day-long technical workshop gives female faculty, research scientists, and graduate students in the machine learning community an opportunity to meet, exchange ideas and learn from each other. The workshop started at the 2006 Grace Hopper Celebration and moved to NeurIPS in 2008. A History of WiML poster was created in 2015 to celebrate the 10th workshop. In 2020, WiML introduced an “un-workshop” at ICML based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. The overall goal of the un-workshop is to advance research through collaboration and increased interaction among participants from diverse backgrounds. Professional and technical Open to all genders and inclusive Positive and productive WiML ORGANIZERS Gratitude to Workshop Organizers: WiML's Foundation and Inspiration WiML was founded in 2006 by Hanna Wallach, Jenn Wortman, and Lisa Wainer, with faculty advisor Amy Greenwald. 2007 workshop organizers: Hila Becker and Bethany Leffler. Faculty advisor Lise Getoor. 2008 workshop organizers: Luiza Antonie, Anna Koop, and Jo-Anne Ting. Faculty advisor Joelle Pineau. 2009 workshop organizers: Finale Doshi, Inmar Givoni, and Farheen Omar. Faculty advisor Daphne Koller. 2010 workshop organizers: Diane Oyen, En-Shiun Annie Lee, and Kate Saenko. Faculty advisor Marie desJardins. 2011 workshop organizers: Nevena Lazic, Monica Babes-Vroman, Rongjing Xiang. Faculty advisor Hanna Wallach. 2012 workshop organizers: Tamara Broderick, Minmin Chen, Pallika Kanani, and Tejaswini Narayanan. Faculty advisor Raquel Urtasun. 2013 workshop organizers: Jennifer Healey, Katie Kinnaird, Zornitsa Kozareva, Talieh S. Tabatabaei, Sonia Todorova 2014 workshop organizers: Allison Chaney, Marzyeh Ghassemi, Sarah Brown, and Jessica Thompson. 2015 workshop organizers: Abbie Jacobs, Kate Niehaus, Svitlana Volkova, Maithra Raghu, Ramya Ramakrishnan. Website 2016 workshop organizers: Diana Cai, Deborah Hanus, Sarah Tan, Isabel Valera, Rose Yu. Website 2017 workshop organizers: Genna Gliner, Ehi Nosakhare, Maja Rudolph, Danielle Belgrave, Negar Rostamzadeh. Website 2018 workshop organizers: Aude Hofleitner, Audrey Durand, Nyalleng Moorosi, Sarah Poole, Amy Zhang. Website 2019 workshop organizers: Michela Paganini, Sarah Aerni, Forough Poursabzi Sangdeh, Nezihe Merve Gürel, Bahare Fatemi. Website 2020 un-workshop organizers: Fariba Yousefi, Caroline Weis, Tatjana Chavdarova, Mandana Samiei, Larissa Schiavo. Website 2020 workshop organizers: Xinyi Chen, Erin Grant, Kristy Choi, Krystal Maughan, Xenia Miscouridou, Judy Hanwen Shen, Raquel Aoki, Belen Saldias, Melissa Woghiren, Elizabeth Wood. Website 2021 un-workshop organizers: Beliz Gokkaya, Wenshuo Guo, Hadia Mohmmed Osman Ahmed Samil, Berivan Isik, Olivia Choudhury. Website 2021 workshop organizers: Boyi Li, Mariya I. Vasileva, Linh Tran, Akiko Eriguchi, Meera Desai, S. Aga Lee, Jieyu Zhao, Salomey Osei, Sirisha Rambhatlam Geeticka Chauhan, Nwamaka Okafor. Website 2022 un-workshop organizers: Paula Gradu, Vinitra Swamy, Giulia Clerici, Mozhgan Saeidi, Noor Sajid, Yina Lin, Shweta Khushu, Deeksha Shama. Website 2022 workshop organizers: Sergül Aydöre, Konstantina Palla, Gloria Namanya, Beliz Gunel, Mariam Arab, Kimia Nadjahi. Website 2023 un-workshop organizers: Giulia Luise, Priyadarshini Kumari, Stephanie Milani, Tiffany Ding, Arianna Bunnell. Website 2023 workshop orgnizers: Natasa Tagasovska, Kelly Buchanan, Megha Srivastava, Jess Sorrell, Hewitt Tusiime, Eda Okur, Shweta Khushu. Website 2024 symposium organizers: Elizabeth Healey, Maryleen Amaizu, Laya Rafiee Sevyeri, Irene Ballester Campos. Website 2024 workshop organizers: Tiffany Vlaar, Ibtihel Amara, Mehrnaz Mofakhami, Bo Zhao, Milica Cvetkovic, Nikita Saxena, Yolanne Yi Ran Lee, Mahsa Massoud. Website

14th Women in Machine Learning Workshop (WiML 2019) The 14th WiML Workshop is co-located with NeurIPS in Vancouver, British Columbia on Monday, December 9th, 2019. Speakers Logistics Program Call for Participation Committee FAQ Code Of Conduct Machine learning is one of the fastest growing areas of computer science research. Search engines, text mining, social media analytics, face recognition, DNA sequence analysis, speech and handwriting recognition, healthcare analytics are just some of the applications in which machine learning is routinely used. In spite of the wide reach of machine learning and the variety of theory and applications, it covers, the percentage of female researchers is lower than in many other areas of computer science. Most women working in machine learning rarely get the chance to interact with other female researchers, making it easy to feel isolated and hard to find role models. The annual Women in Machine Learning Workshop is the flagship event of Women in Machine Learning . This technical workshop gives female faculty, research scientists, and graduate students in the machine learning community an opportunity to meet, network and exchange ideas, participate in career-focused panel discussions with senior women in industry and academia and learn from each other. Underrepresented minorities and undergraduates interested in machine learning research are encouraged to attend. We welcome all genders; however, any formal presentations, i.e. talks and posters, are given by women. We strive to create an atmosphere in which participants feel comfortable to engage in technical and career-related conversations. Now in its 14th year, the 2019 workshop is co-located with NeurIPS in Vancouver, Canada. Besides this un-workshop and annual workshop which is co-located with NeurIPS, Women in Machine Learning also organizes events such as lunch at ICML and AAAI conferences, maintains a public directory of women active in ML, profiles the research of women in ML, and maintains a list of resources for women working in ML. Invited Speakers Location This workshop takes place at the Vancouver Convention Centre in Vancouver, Canada. The workshop will take place in East Hall C . The poster sessions will take place in East Hall B . An important note on the NeurIPS registration WiML registration is separate from NeurIPS registration, and does not gain you access to any part of NeurIPS, whether the main conference, workshop, tutorials, or industry expo. You would still need to register separately for NeurIPS – their registration process can be found on their website . WiML Room Layout for Lunch / Mentorship Tables Logistics and finding roommates You may take advantage of NeurIPS group hotel rates, provided here . Book your accommodation as soon as possible as the discounted room blocks are being filled up quickly. In the past, workshop participants have also used Airbnb and hostels . Hotel cancellation policy should be checked with the hotels. WiML is not responsible for information provided on external websites. To find a roommate, please enter you information in this form , visualize the results here and contact other participants. In addition, you can get in touch with others on the WiML network . Childcare NeurIPS is kindly providing free onsite childcare to participants this year. If you only have a WiML registration, you can still use NeurIPS’s childcare on Sunday December 8 and Monday December 9. To access childcare from Tuesday on, NeurIPS registration will be required. For more information on how to register for the childcare service, please visit the NeurIPS childcare page . Visa NeurIPS has compiled instructions and information about the visa application process (see this link ). A visa invitation letter comes with the NeurIPS registration. If you don’t have the NeurIPS visa invitation letter, we can also provide you invitation letters upon successful registration to the WiML workshop. PROGRAM MENTORSHIP TABLES ACCEPTED POSTERS The 2019 WiML Workshop will be held on Monday, Dec 9th, 2019 in Vancouver, Canada. Workshop activities primarily take place in Vancouver Convention Center East Exhibition Hall C , with the exception of the poster sessions which will take place in Vancouver Convention Center East Exhibition Hall B . A pre-workshop reception will be held the night of Sunday, Dec 8th, 2019 from 7:30pm to 10:00pm in the Pinnacle Ballroom, Vancouver Marriott Pinnacle Downtown Hotel, 1128 W Hastings St, Vancouver, BC, V6E 4R5. Separate advance registration is required for the reception (see Eventbrite ), and there won’t be onsite registration. All participants are required to abide by the WiML code of conduct . Call for Participation The 14th WiML Workshop is co-located with NeurIPS in Vancouver, Canada on Monday, December 9th, 2019. The Workshop for Women in Machine Learning is a one-day event with invited speakers, oral presentations, and posters. The event brings together members of the academic and industry research landscape for an opportunity to connect and exchange ideas, and learn from each other. There will be a mentoring session to discuss current research trends and career choices in machine learning. Underrepresented minorities and undergraduates interested in pursuing machine learning research are encouraged to participate. While all presenters will identify primarily as female or nonbinary, all genders are invited to attend. Submission is now closed. Please check back for information on how to register as an attendee. IMPORTANT DATES July 15th, 2019 – Abstract Submission Open on CMT August 15th, 2019 11:59pm PT – Abstract Submission Deadline September 1st, 2019 – Visa-Friendly (Early) Notification of Acceptance and Travel Funding September 21st, 2019 – Regular Notification of Acceptance October 15th, 2019 – Regular Notification of Travel Funding November 21st, 2019 – Registration Deadline (or earlier, if we sell out) December 9th, 2019 – WiML Workshop Day This year, WiML is introducing a Visa-Friendly (Early) notification of acceptance and travel funding on September 1, 2019. If you need to apply for a visa to travel to Canada, we encourage you to select this option in the submission page in CMT. If you do not need to apply for a visa to travel to Canada, please do not select this option. SUBMISSION INSTRUCTIONS We strongly encourage students, postdocs, and researchers who primarily identify as women or nonbinary in all areas of machine learning to submit an abstract (1 page PDF) describing new, previously, or concurrently published research. We welcome abstract submissions in theory, methodology, as well as applications. Abstracts may describe completed research or work-in-progress. While the presenting author need not be the first author of the work, we encourage authors to highlight the contribution of authors who identify primarily as female or nonbinary — particularly the presenting author — in the abstract. Authors of accepted abstracts will be asked to present their work in a poster session. Authors with multiple accepted posters will be asked to select only one poster to present. A few authors will be selected to give spotlight or oral presentations. There are no formal proceedings. Submissions will be peer-reviewed in a double-blind setting. After submission, all authors will automatically receive an invitation for the reviewer pool, into which they can opt-in. Many student and postdoc authors who review for WiML will be eligible for travel funding (see further details below). Submission page: https://cmt3.research.microsoft.com/WiML2019 (Submission is now open!) Style guidelines: Abstracts must not include identifying information. Abstracts must be no more than 1 page (including any references, tables, and figures) submitted as a PDF. Main body text must be minimum 11 point font size and page margins must be minimum 0.75 inches (all sides). Do not include any supplementary files with your submission. Content guidelines: Your abstract should stand alone, without linking to a longer paper or supplement. You should convey motivation and give some technical details of the approach used. While we acknowledge that space is limited, some experimental results are likely to improve reviewers’ opinions of your paper. Acceptance criteria: All accepted abstracts must be presented by authors who identify primarily as female or nonbinary. Abstracts will be reviewed by multiple reviewers, who will use the following criteria: Is this abstract appropriate for WiML? I.e., does it describe novel research or an interesting application in machine learning or related fields? Does the abstract stand alone? Does the abstract adequately convey the material that will be presented? Examples of accepted abstracts from previous years can be found here , and advice on writing a one-page abstract can be found here . Due to the volume of submissions anticipated, we are unable to review any submitted materials besides the requested abstract. TRAVEL FUNDING Registration for WiML is free. Travel funding is available for presenting authors. To qualify, the author must be a student, postdoc, or equivalent position (equivalent positions include unemployed recent grads and early career researchers from underrepresented geographical areas), identify primarily as female or nonbinary, have an accepted abstract, and review for WiML. The amount of the travel funding varies by the author’s geographical location and the total amount of funding WiML receives from sponsors. In the past, funding ranging from $300-$1000 has been given. WiML travel funding is administered as reimbursements after the workshop and no funding is allocated before the workshop. If you are attending NeurIPS, we also encourage you to apply for NeurIPS’ volunteering and travel funding opportunities, which are separate and independent of WiML travel funding. Check the NeurIPS website directly for details. AREA CHAIRS If you are interested in being an area chair, please fill in the application here . The area chairs must identify primarily as female or nonbinary. The role of area chairs is to evaluate the reviews, write a final meta-review and suggest an accept/reject decision for each abstract. We expect each area chair to be responsible for up to 10 one-page abstracts. ORGANIZERS Sarah Aerni (Salesforce) Nezihe Merve Gürel (ETH Zurich) Michela Paganini (Facebook AI) Forough Poursabzi-Sangdeh (Microsoft Research) Questions? Check out the FAQs or reach us at wiml2019[at]wimlworkshop[dot]org PLATINUM SPONSORS GOLD SPONSORS SILVER SPONSORS BRONZE SPONSORS SUPPORTER We gratefully acknowledge support for participant travel from: Committee ORGANIZERS Michela Paganini Postdoctoral Researcher, Facebook AI Research Connection Chair Bahare Fatemi Forough Poursabzi-Sangdeh Postdoctoral Researcher, Microsoft Research Senior Program & Mentorship Chair Nezihe Merve Gürel PhD Student at ETH Zurich Sarah Aerni Director of Data Science, Salesforce Finance & Sponsorship Chair WiML 2019 Reception Organizers Srishti Yadav (Research Scholar, Simon Fraser University) Meha Kaushik (Software Engineer, Microsoft) Diversity and Inclusion Chair Danielle Belgrave, Principal Research Manager at Microsoft Research Supervolunteers We would like to acknowledge and warmly thank our super-volunteers who worked tirelessly to ensure a high quality un-workshop. Belen Saldias, MIT Elre Oldewage, University of Cambridge Mandana Samiei, McGill and Mila Niveditha Kalavakonda, University of Washington Seattle Weiwei Zong, Henry Ford Health System FAQs Do you have a list of members? How can I join WiML? WiML doesn’t have “members” per se, any women working in machine learning can be part of the WiML network. We have a mailing list for anyone to post announcements of interest to the WiML network and an opt-in, necessarily incomplete directory of women working in machine learning . How can I join the WiML mailing list? Join the mailing list directly here . What kind of events do you organize? Our flagship event is the annual WiML Workshop, typically co-located with NeurIPS, a machine learning conference. We also organize an “un-workshop” at ICML, as well as small events (e.g. lunches and receptions) at other machine learning conferences, such as CoRL, COLT, etc. Check out our events page for up-to-date listings of events. Do you have local meetups? No, but check out WiMLDS (website, Twitter), another organization that supports women in machine learning by organizing local meetups. How do I reach the WiML network? Use our mailing list . How can I sponsor WiML? Thank you for your interest in sponsoring WiML! See this page for more information. I am looking for an invited speaker / panelist / area chair / program committee member etc. Can WiML help me? Use our directory of women in machine learning or post this opportunity to our mailing list . I want to circulate a job posting. Can WiML help me? Post directly to our mailing list . How can I support WiML? You can: Post interesting opportunities and job postings to our mailing list . Use our directory of women in machine learning to find invited speakers, panelists, area chairs, program committee members, etc, or post these opportunities to our mailing list . Sponsor us. See this page for more information. Volunteer at one of our events. Check out our events page for up-to-date listings of events. Apply to be an area chair or reviewer at WiML Workshop (see this year’s workshop website for info). Take pictures at our events and share with us (tag @wimlworkshop on Twitter). If you see us mentioned in the media, send us a link at info@wimlworkshop.org . And many others! How did WiML start? What's the founding story? Hanna Wallach, Jennifer Wortman Vaughan, Lisa Wainer, and Angela Yu shared a room at NIPS 2005. Late one night, they talked about how exciting it was that there were FOUR female students at NIPS that year. They tried to list all the women in machine learning they know of and got to 10, then started talking about creating a meeting or gathering for all these women and perhaps others that they didn’t know about. Jenn, Lisa, and Hanna put together a proposal for a session at the 2006 Grace Hopper Celebration of Women in Computing that would feature talks and posters by female researchers and students in machine learning. The 1st WiML workshop was co-located with the 2006 Grace Hopper Celeberation. In 2008, WiML Workshop moved to NIPS (renamed NeurIPS in 2018) and there has been a WiML Workshop at NeurIPS every year since. In 2020, WiML introduced an “un-workshop” at ICML based on the concept of an “un-conference”, a form of discussion on a pre-selected topic that is primarily driven by participants. Read more WiML history here ! I am a man. Can I attend WiML? Yes. Allies are welcome to attend! Note, however, that all speakers and poster presenters will primarily identify as women, nonbinary, or gender-nonconforming, as our goal is to promote them and their work within the machine learning community. What are the mentorship roundtables? Each table seats 8-10 people (including mentors), with two mentors leading the discussion on a particular topic at each table. WiML attendees rotate between tables every 15-20 minutes. This allows attendees to gain exposure to different topics, and mentors to meet a large number of WiML attendees. Is WiML an archival venue? No, WiML is a non-archival venue. This means that, if your contribution is accepted, we will not be asking you to submit a camera-ready version of it, nor will we publish it anywhere (neither online nor in proceedings of any sort). We will only make the title and authors’ names available in the program book. I have a question that isn't answered here. How do I reach you? We receive a lot of email. Help us help you by reaching out through the appropriate channels. Job posting, announcement, CFP, etc: Post directly to WiML mailing list . Have event pictures to share: post on Twitter and tag @wimlworkshop Workshop enquiries: workshop@wimlworkshop.org If you are a company interested in sponsoring WiML: sponsorship@wimlworkshop.org Any other enquiries: info@wimlworkshop.org If you email us, don’t cc multiple email addresses — this saves us time routing your email to one mailbox, and reduces the chances of your email getting lost. Thank you in advance! Back To Top

DIRECTORY We maintain a directory of women working in machine learning Are you a woman or gender minority in the field of Machine Learning? Add yourself to the Directory by creating an account here . After confirming your email address (check your spam or promotion folder for the confirmation email!), log in and create a “Public Profile”. Create Account The directory is opt-in: you need to confirm your account and create a Public Profile, selecting the option to appear in the directory to be listed. Please note that if you signed up on the previous directory (through Google forms), you will need to create a new profile. For searching the directory, use this link and filter with space-separated keywords. Additional filters allow you to search for senior positions (academia and industry), and/or for countries that are typically under-represented in research (based on the 2017 list of low to middle-high income countries). If you are organizing an event related to machine learning, please use the directory to look for invited speakers, area chairs, conference committee members, etc. For more details on the directory, please see the FAQ . Feedback on the Directory can be provided through this form .