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Empowering Women in Machine Learning: Amplifying Achievements, Elevating Voices, Building Leaders, and Bridging Gaps to enhance the experience of women in machine learning. 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

Empowering Women in Machine Learning: Amplifying Achievements, Elevating Voices, Building Leaders, and Bridging Gaps to enhance the experience of women in machine learning. 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

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 4th Women in Machine Learning Un-Workshop, ICML 2023 4th Women in Machine Learning Un-Workshop, ICML 2023 The 4th WiML Un-Workshop is co-located with ICML on Friday, July 28th, 2023. Speakers Logistics Program Call for Participation Committee FAQ 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 Un-Workshop is the flagship event in un-conference style 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 4th year, the 2023 un-workshop is co-located with IC ML . Besides this annual un-workshop, Women in Machine Learning also organizes annual workshop at NeurIPS, 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. All participants are required to abide by the WiML Code of Conduct . I'm a paragraph. Click here to add your own text and edit me. It's easy. Invited Speakers Rihab Gorsane Jennifer Doudna Joelle Pineau Location This workshop will be in-person only, co-located with ICML at the Hawaii Convention Centre , Honolulu. 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. PROGRAM PANELISTS BREAKOUT SESSIONS COFFEE MEET & MINGLE SOCIAL The program follows the following color scheme: talks , breakout sessions , program breaks , sponsor round table , and panel discussion . The schedule is in local time zone (HST) . The program book is available at Program Book 2023 . 09:15 - 09.30 [Introduction & Opening Remarks - Priyadarshini Kumari (Sony AI) and Giulia Luise (Microsoft) - Hall 316C ] 09:30 - 10.00 [Invited Talk - Joelle Pineau (Meta AI and McGill University, Canada)] A culture of open and reproducible research in the era of large AI generative models - Hall 316C ] We have seen in the last year an incredible pace of progress in large AI models, with increasing abilities to generate high-quality images, videos, text, sound, and more. The best of these models display signs of creativity, reasoning, generalization, and plasticity beyond what we could imagine just a few years ago. Yet many challenges and open questions remain, both on the technological aspects and the societal impact of these models. Further progress, especially in mitigating the social risks of these models, is hampered by a lack of transparency and reproducibility. In this talk, Joelle will describe ongoing efforts to increase best practices towards the responsible training and deployment of AI research systems, drawing on her experience with the ML reproducibility program and the recent release of several state-of-the-art large models. 10.00 - 10.30 [Coffee Break and Networking] 10:30 - 11.00 [Invited Talk - Jennifer Doudna (UC Berkeley, USA)] Science and Snorkeling: My Journey with CRISPR - Hall - 316C ] In this talk, Jennifer will discuss her professional and personal journey working on CRISPR technology, from its genesis to its applications today, and focus on ethical challenges that mirror challenges with AI/ML. 11:00 - 12:00 [ Breakout session #2 (Three parallel sessions)] 1. 1) Leveraging Large Scale Models for Identifying and Fixing Deep Neural Networks Biases . [Hall 316C] Leader: Polina Kirichenko, Co-leads: Reyhane Askari Hemmat, Megan Richards. Facilitators: Vitória Barin Pacela , Mohammad Pezeshki 1. 2) The Role of Mentorship and Building Long-term Professional Relationships. [Hall 326A] Leader: Arushi Jain. Co-leads: Sangnie Bhardwaj Facilitators: Motahareh Sohrabi , Padideh Nouri 1. 3) Robustness in Machine Learning. [Hall 326B] Leader: Yao Qin. Co-lead: Qi Lei Facilitators: Christina Baek 12:00 - 13:30 [ Lunch and Sponsor Round Table Hall 316C ] Round Table A: Apple -- Finding Mentors and Being a Mentor Rishika Agarwal ( Engineer) Ivy Zhang (Engineer) Round Table B: D. E. Shaw Research -- Machine Learning at D. E. Shaw Research Jocelyn Sunseri (Machine Learning Research Engineer) Round Table C: Google DeepMind -- Keeping Up With the Pace of Change in Industry Kate Baumli (Research Engineer) Kavya Kopparupu (Research Engineer) Round Table D: Google Research -- Life and Work at Google Alicia Parrish (Research Scientist, Responsible AI) Round Table E: Microsoft -- Exploring Pathways: Career Opportunities, Growth, and Work-Life Balance at Microsoft Research Lili Wu (Data and Applied Scientist, Microsoft Research) Cyril Zhang (Senior Researcher, Microsoft Research) Round Table F: Two Sigma -- Your Next Big ML Move: Innovation in Finance Brittany Clarke (Diversity Recruiting Program Manager) Alyssa Lees (Engineering Manager, News Engineering: a NLP Technology Team) 13:30 - 14:00 [Invited Talk - Rihab Gorsane (Instadeep, Tunisia)] My journey at an African AI startup - Hall 316C ] In the talk, Rihab will share her personal journey as a mid-career woman coming from Africa in the field of Artificial Intelligence (AI) and highlight the remarkable experiences she has gained working at an African AI startup. With a focus on both technical accomplishments and driving forces that have propelled her forward, I aim to inspire the audience while providing valuable insights into her professional growth - particularly to women who aspire to build their careers in AI. 14:00 - 15:00 [ Breakout session #3 (Three parallel sessions)] 2. 1) Key Challenges for Applicable Reinforcement Learning . [Hall 316C] Leader: Fengdi Che. Co-leads: Arushi Jain Facilitators: Yueying Tian 2. 2) Data Diversity and Downstream Impact. [Hall 326B] Leader: Judy Shen. Co-lead: Paula Gradu Facilitators: Kristina Ulicna 2. 3) Deploying Research and Making Real-world Impact [Hall 326A] Leader: Fei Fang. Co-leads: Diyi Yang Facilitators: Bingbin Liu 15.00 - 15.30 [ Coffee Break and Networking] 15:30 - 16:30 [ Panel Discussion: Fostering Women's Leadership in the Realm of Emerging Trends and Technologies - Hall 316C ] Panelists: Joelle Pineau (Meta, McGill University), Pascale Fung (HKUST), Yao Qin (UC Santa Barbara, Google Research), Rihab Gorsane (Instadeep) Moderator: Natasa Tagasovska (Prescient Design, Genentech) The panel session will comprise 45 minutes of moderated discussion and a 15-minute Q&A with the audience. The session aims to bring together two significant themes: advancing women's leadership in AI and the future of machine learning with its emerging trends and technologies. The discussion will focus on empowering women in AI leadership positions to navigate these emerging trends effectively and reshape the landscape of AI. 16:30 - 16:45 [President Remarks: Sarah Tan (Cambia Health, Cornell University) - Hall 316C ] Joelle Pineau Joelle Pineau is the Vice President of AI Research at Meta, supporting labs across North America and Europe. She is also a faculty member at Mila and a Professor and William Dawson Scholar at the School of Computer Science at McGill University, where she co-directs the Reasoning and Learning Lab. She holds a BASc in Engineering from the University of Waterloo, and an MSc and PhD in Robotics from Carnegie Mellon University. Dr. Pineau's research focuses on developing new models and algorithms for planning and learning in complex partially-observable domains, and on applying these algorithms to complex problems in robotics, health care, games and conversational agents. Learn more about her work at: https://www.cs.mcgill.ca/~jpineau/ Pascale Fung Pascale Fung is a Chair Professor at the Department of Electronic & Computer Engineering at The Hong Kong University of Science & Technology (HKUST), and a visiting professor at the Central Academy of Fine Arts in Beijing. She is an elected Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) for her "significant contributions to the field of conversational AI and to the development of ethical AI principles and algorithms", an elected Fellow of the Association for Computational Linguistics (ACL) for her significant contributions towards statistical NLP, comparable corpora, and building intelligent systems that can understand and empathize with humans. She is a Fellow of the Institute of Electrical and Electronic Engineers (IEEE), an elected Fellow of the International Speech Communication Association and the Director of HKUST Centre for AI Research (CAiRE), an interdisciplinary research centre on top of all four schools at HKUST. Learn more about her work at: https://pascale.home.ece.ust.hk/ Yao Qin Yao Qin is an Assistant Professor at the Department of Electrical and Computer Engineering at UC Santa Barbara, affiliated with the Department of Computer Science. She is also a senior research scientist at Google Research. She obtained her PhD degree at UC San Diego in Computer Science in 2020 and worked at Google Research afterwards. Her research interests primarily focus on robustness in multi-modality models, fairness in generative modeling and AI for healthcare, particularly for diabetes. She has served as Area Chair for ICLR-2023 and ICCV-2023 and co-local Chair for KDD-2023. In addition, she has been recognized as EECS Rising Star at MIT, 2021. Learn more about her at: https://www.ece.ucsb.edu/people/faculty/yao-qin Rihab Gorsane Rihab Gorsane is a Research Engineer and a team lead at InstaDeep. She is currently working on Reinforcement Learning based projects for industrial applications where she is helping to automate the scheduling, routing, and dispatching of trains at a large scale for a national rail operator. Rihab is also involved in research projects within the company focusing on Multi-Agent RL evaluation. She is passionate about AI skills development in Africa, is a Google developer expert in Machine Learning, and has taught DL/RL courses at Tunisian universities. Nataša Tagasovska (moderator) Nataša is a Senior Machine Learning Scientist at Prescient Design, Genentech since January 2022 where she joined the effort of applying ML to accelerate drug design. Her research interests are related to causal learning, generative models and multi-property optimization. Before she was a Senior Data Scientist at the SDSC at EPFL-ETHZ where she worked on translational projects applying ML to domain-specific and social science research efforts. She holds a PhD in Statistics from University of Lausanne and a BS and MSC in Computer Science and Engineering. During her studies she interend at Facebook (Meta) AI Research and NATO. During the day of the WiML Un-Workshop @ ICML 2023 there will be three different Breakout Sessions slots! We list the sessions, topics, leaders, and facilitators. Breakout Session #1 (11.00 - 12.00 HST) Leveraging Large Scale Models for Identifying and Fixing Deep Neural Networks Biases [Hall 316C] Leader: Polina Kirichenko Co-leads: Reyhane Askari Hemmat, Megann Richards Facilitators: Vitória Barin Pacela , Mohammad Pezeshki The Role of Mentorship and Building Long-term Professional Relationships [Hall 326A] Leader: Arushi Jain Co-leader: Sangnie Bhardwaj Facilitators: Motahareh Sohrabi, Padideh Nouri Robustness in Machine Learning [Hall 326B] Leader: Yao Qin. Co-leads: Qi Lei Facilitator: Christina Baek Breakout Session #2 (14.00 - 15.00 HST) Key Challenges for Applicable Reinforcement Learning [Hall 316C] Leader: Fengdi Che Co-leader: Arushi Jain Facilitators: Yueying Tian Deploying Research and Making Real-world Impact [Hall 326A] Leader: Fei Fang Co-leads: Diyi Yang Facilitator: Bingbin Liu Data Diversity and Downstream impact [Hall 326B] Leader: Judy Shen Co-leads: Paula Gradu Facilitator: Kristina Ulicna During the workshop program, there are two "program breaks" listed in the agenda : one in the morning (10:00 - 10:30 HST), and one in the afternoon (15:00 - 15:30 HST). These program breaks as an excellent opportunity to facilitate optional community-building activities for workshop attendees. We chose the coffee break activities inspired by the following principles: Optional participation . For both coffee breaks, participation will be encouraged, but is optional. Attendees who wish to simply "take a break" can stay in the room and not participate in the activities. We also have organized activities where participants can organically "walk away" and engage in other conversations at any time. Ease . We also hope to facilitate as low of a barrier to participation as possible, by reducing logistical barriers whenever possible (i.e. holding activities in the same room as the next talk). Inclusivity . We understand that WiML attendees are in significantly different places in their career. We've attempted to design all activities so that all attendees can participate, regardless of their seniority or experience working in ML. We hope the activities can facilitate new connections. Morning Coffee Break: Ask Me About (AMA)... Location: Main Room, 316C TL;DR: Learn from & with your fellow WiML attendees by completing an "Ask Me Anything" name tag at the registration desk! No matter where you are in your career, you never know how your experiences may be helpful to another attendee. Afternoon Coffee Break: Bingo Location: Main Room, 316C TL;DR: Make new friends & connections during our WiML "bingo" icebreaker game! We'll have prizes for the first attendees to finish their cards. Please join us for a reception hosted by the Women in Machine Learning (WiML) organization. The reception will take place before the WiML 2023 Un-Workshop on Thursday, July 27th, from 6 pm - 9 pm HST at Hawaiian Brian's , down the street from the Hawaii Convention Center. Dinner and drink tickets will be provided . Important notes: Registration for this reception is separate from registration for the workshop. To attend the reception, please register here . Due to extremely limited capacity, we ask that you only register if you are committed to attending. Registration is free. Do register early, as we may reach capacity soon. All participants are required to abide by the [WiML Code of Conduct] . 18:25-18:30 (5 minutes) Intro by Arianna Bunnell 18:35-18:50 (15 minutes) Remarks by Frankie Zhu (Assitant Professor at University of Hawaii) 18:50-18:55 (5 minutes) Remarks by Sarah Tan (WiML President, Cambia Health, Cornell University ) Call for Participation WiML 4th Un-Workshop @ ICML 2023 [submissions are now closed ] The Women in Machine Learning will be organizing the fourth un-workshop at ICML 2023. 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 discussions. This is an event format to encourage more participant interaction and we are excited to be able to explore this format fully in-person this year! This year’s goal: the purpose of the un-workshop is to bring together researchers who identify as a woman, non-binary and/or gender non-conforming, fostering an environment for constructive discussions on research and career advancement. This year we particularly encourage mid-career researchers that identify as a woman, non-binary and/or gender non-conforming participate and contribute in the un-workshop! However, everyone, regardless of their career stage or gender, is warmly welcomed to participate and join in the discussions! We'd love for you to submit a one-page proposal to lead one of the breakout sessions. This is just one of the many ways you can contribute to the conversation - check out the other options below! While the presentations will be led by woman, non-binary and/or gender non-conforming individuals, all genders are invited to attend! IMPORTANT DATES June 3rd, 2023 -- Application Form opens! June 19th, 2023 June 24th, 2023 -- Deadline ( Anywhere on Earth ) to apply for a breakout session, registration fee funding, or volunteering June 24th, 2023 June 30th 2023 -- Notification of acceptance for all of the above (midnight Anywhere on Earth ) July 28th, 2023 -- WiML Un-Workshop Day Participate in the WiML Unworkshop Lead or engage in a breakout session : submit a proposal to lead a breakout session on a certain topic, either research oriented or about career development. Volunteer : seize this opportunity to contribute to the success of this WiML event! Help is needed with the technical setup and to fulfill the diverse needs that pop up during the event! Attend : participate in breakout session discussions, attend talks and/or panel discussions, come around for a chat with coffee! 1. Breakout session proposals: A breakout session is a 1-hour free-form discussion overseen by 1-3 leaders , with contributions from named participants, and with assistance from 1-2 facilitators to take notes and encourage participant interactions. We strongly encourage women, nonbinary and/or gender non-conforming individuals in all areas of machine learning to submit a proposal to lead or be a named participant in a topical breakout session. Compared to breakout sessions in previous years, we are making the following exciting changes for this year! First, we are expanding beyond technical and research topics. This year, we also encourage proposals related to growth , career development, and other non-technical topics that would be of interest to women, non-binary and/or gender non-confirming individuals in ML (particularly those who are mid-career). Second, we are introducing a new way of participating in breakout sessions: named participants . If you have an interesting idea or project that you think can spark productive discussion, or there is a topic that really interest you and you would be up for discussing it, we encourage you to submit a summary/position paper/poster. This can include both technical and non-technical topics. If there is a good match between your submission and the breakout session proposals, you will be matched with a breakout session leader and asked to contribute to the breakout session as a named participant. The exact nature of your contribution will be determined by your assigned session leader. You may apply to be a breakout session leader and/or apply to be a named participant. Guidance for applying to be a breakout session leader : your one-page proposal PDF should include a description of your proposed topic, why it is important/relevant, potential discussion questions, and how you would incorporate named participants (as described above). Guidance for applying to be a named participant : identify a topic, idea, or project that would be a good starting point for a discussion. This can be anything ranging from a summary of the topic and why you think it is relevant for WiML community, an unpolished idea, or a completed research project. Focus on explaining how your idea/project is relevant to a broader audience and what questions it sparks. Submissions must be one-page PDFs. Try to explain in simple language with minimal technical jargon. More information for leaders: A complete proposal consists of a 1 page PDF, along with the names and bios of leaders and facilitators submitted separately in the application form . Proposals need not be anonymized. We strongly recommend having at least 2 leaders, with a diverse set of leaders preferred (see selection criteria below). The names of facilitators should also be provided. WiML registration fee funding is prioritized for accepted breakout session leaders who fulfill certain eligibility criteria (see details below). 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 identify as a woman, non-binary and/or gender non-conforming Point-out key characteristics of your topic and make connections with other topics Describe the key challenges and approaches in this research area or career topic on a high-level 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. 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. More information for named participants: Guidelines for and roles of named participants: Breakout session named participants must be women, non-binary and/or gender non-conforming Point out key characteristics of your topic and make connections with other topics. Describe how your work or knowledge contributes to this area. 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 Submission instructions: 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, and specifics around how you could contribute to the conversation. 2. Volunteering: We are seeking volunteers to help with technical setup and help during the event. You can indicate if you can help in any way in the 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. 3. 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 19, 2023 . Selected breakout session leaders, breakout session participants, 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 topic preferences. All participants are required to abide by the WiML Code of Conduct. 4. Registration fee funding: To apply for funding, you should identify as a woman, non-binary and/or gender non-conforming and commit to participating in at least one breakout session as a leader, named participant, 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 or named participants who do not have other sources of registration fee funding will be prioritized for WiML funding. Other participants are also encouraged to apply. In your application, please indicate any funding sources you may have and how WiML's support is needed. Please note that WiML is able to fund registration fees only (not travel and accommodation) for selected participants. Further questions? Check out the FAQs ( https://wimlworkshop.org/faq/ ) or reach us at workshop@wimlworkshop.org 5. A sneak peak of other activities that the workshop will host: We will give more details closer to the event but the workshop will include a sponsor roundtable, where you will have the opportunity to interact and network with our sponsors. Furthermore, we will facilitate networking, mentoring, and impromptu discussions during the event . Stay tuned! PLATINUM SPONSORS PLATINUM SPONSORS PLATINUM SPONSORS Committee ORGANIZERS Giulia Luise General Chair Priyadarshini Kumari Senior Program Chair Stephanie Milani Breakout Program and Logistics Co-Chair Tiffany Ding Finance and Sponsorship Chair WiML RECEPTION ORGANIZER Arianna Bunnell Social Chair ADVISORY Danielle Belgrave D&I chair Bahare Fatemi D&I chair Mandana Samiei WiML Board POC SUPER VOLUNTEERS Mojgan Saeidi Nari Johnson FAQs How do I participate to the un-workshop? Start with filling the application form , especially if you are interested in presenting! The workshop will take place on July 28th 2023, co-located with ICML at the Hawaii Convention Centre in Honolulu. We will give more details nearer to the event. Does registering for the WiML un-workshop also mean I'm registered for ICML? Unfortunately not. You would still need to register separately for ICML – their registration process can be found here. You should only register to ICML if you are interested in attending ICML activities beyond WiML un-workshop. What does un-workshop mean? 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. Please check our Call for Participation for more details! How much travel funding is available? We will be able to sponsor the ICML registration fee for selected participants. Please fill the application form to apply for funding! 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 a man. Can I attend WiML un-workshop? 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? We will update the format of this year's Sponsor Roundtable closer to the event! Is WiML an archival venue? No, WiML is a non-archival venue. Moreover, the un-workshop format does not include paper submissions. Check the Call for Participation to learn how to contribute to the un-workshop! Is there a Code of Conduct? Yes, you can find it here . 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!

Empowering Women in Machine Learning: Amplifying Achievements, Elevating Voices, Building Leaders, and Bridging Gaps to enhance the experience of women in machine learning. 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

< Back Jessica Montgomery WiML Vice President of Research & Policy (2020-2021), Director (2019-2020, 2021-2022) Visit my Profile

We maintain a mailing list for the Women in Machine Learning network. Are you a women working in machine learning? Join our mailing list. Post directly in our mailing list. Mailing List We maintain a mailing list for the Women in Machine Learning network. Are you a women working in machine learning? Join our mailing list . Have a job posting, announcement, call for participation, etc? Post directly in our mailing list. The mailing list is intended for any female student, postdoc, academic researcher, industrial researcher, and anyone else who wants to post content relevant for this community. You do not have to be a woman to join the mailing list. Please post job postings, announcements, calls for participation, etc. directly to the mailing list. You can also use the mailing list to look for roommates at conferences, discuss machine learning topics, etc. Join/Post to Mailing List

< Back Savannah Thais, PhD WiML Director (2019-2021)