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< Back Nezihe Merve Gürel, PhD WiML Vice President of Events (2022-2023), Director (2021-2022) Visit my Profile

WiML Statements and Calls serves as a repository for the organization's official statements on inclusivity and related matters, as well as announcements for leadership and employment opportunities within WiML. This page reflects WiML's dedication to promoting diversity, ethical standards, and active participation in leadership roles within the machine learning community.​ WiML+2 WiML Statements and Calls Statements on Inclusivity from WiML: First Statement from the Women In Machine Learning Executive Board on Inclusivity Second Statement from the Women In Machine Learning Executive Board on Inclusivity Black Lives Matter Statement for Dr. Timnit Gebru Calls for WiML Board of Directors: 2022 WIML Board of Directors–General 2021 WiML Board of Directors — General 2019 WiML Board of Directors — Policy and Research Committee 2019 WiML Board of Directors — Treasurer Call for WiML Full-time Employee: 2021 WiML Operations Administrator

< Back Judy Hanwen Shen WiML Director Visit my Profile

< Back Keren Gu WiML Director (2019-2022) Visit my Profile

< Back Jo-Anne Ting, PhD WiML Treasurer (2009-2012) Visit my Profile

< Back Claire Monteleoni, PhD WiML Director (2010-2012) Visit my Profile

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

Empowering Women in Machine Learning: Amplifying Achievements, Elevating Voices, Building Leaders, and Bridging Gaps to enhance the experience of women in machine learning. 17th Women in Machine Learning Workshop (WiML 2022) The Workshop is co-located with NeurIPS on Monday, November 28th, 2022 at the New Orleans Convention Center in Louisiana, USA. 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 17th year, the 2022 workshop is co-located in-person with NeurIPS 2022 at New Orleans in Louisiana. Besides this annual workshop, 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 Alice Oh KAIST University Hima Lakkaraju Harvard University Bianca Zadrozny IBM Research Raesetje Sefala Distributed AI Research Institute Location The in-person workshop will be co-located with NeurIPS in New Orleans, Louisiana on Monday, November 28th, 2022 . Poster Dimensions In person event: Please pay attention to the dimensions of your posters. Workshop posters should be printed on thin paper, not laminated and no larger than 24 inches wide x 36 inches high. See more info regarding workshop poster dimensions here . Virtual event: (details coming soon) Childcare NeurIPS is kindly providing free onsite childcare to participants this year. Registration to the NeurIPS conference is required to participate in this year's WiML workshop. For more information on how to register for the childcare service, please visit the NeurIPS website. Information for authors Authors may find information about poster preparation and upload here . We are looking forward to welcoming you! Head over to our program webpage to check the list of speakers! In order to attend our event, you complete both steps below: Register to NeurIPS 2022 here . To attend our in-person event, you need to register either for the NeurIPS “Conference” or Workshops” session. Registering for either physical component will grant you access to WiML . If you are attending virtually, the NeurIPS “Virtual Only Pass” will suffice. Early registration (with reduced rates) ends on October the 14th. Information about visa application, including visa letter invitation can be found here . Fill in our WiML registration form (free) here . This form helps us keep a headcount. Authors, co-authors and volunteers will be receiving promo codes by email. Authors and co-authors of accepted abstracts, area chairs and volunteers, should have already received promo-codes they may add during the Eventbrite registration. Note: Any physical (in-person) registration includes the NeurIPS ‘Virtual Only Pass’ and as such provides a pass to our virtual component too. Attendees that are attending only virtually using the NeurIPS “Virtual Only Pass” registration, they will be able to access the livestream links of our in-person event. PROGRAM PANELISTS MENTORSHIP ROUNDTABLES LIST OF ACCEPTED POSTERS LIST OF REVIEWERS Monday, November 28, 2022 [in-person] (Time in CT) Morning Session 7:30 am - 8:30 am Registration & Breakfast 8:30 am - 8:45 am Opening Remarks - Konstantina Palla (Senior Program Chair) 8:45 am - 9:00 am D&I Chair remarks - Danielle Belgrave 9:00 am - 9:10 am Contributed talk (Tejaswi Kasarla ) - "Maximum Class Separation as Inductive Bias in One Matrix" 9:10 am - 9:20 am Contributed talk (Taiwo Kolajo ) - "Pre-processing of Social Media Feeds based on Integrated Local Knowledge Base" 9:20 am - 9:55 am Invited talk - Alice Oh - "The importance of multiple languages and multiple cultures in NLP research" 9:55 am - 10:10 am Coffee break 10:10 am - 10:25 am WiML Board Remarks - Jessica Schrouff 10:25 am - 11:00 am Invited talk - Raesetje Sefala - "Constructing visual datasets to answer research questions" 11:00 am - 11:10 am Contributed talk (Pascale Gourdeau ) - "When are Local Queries Useful for Robust Learning?" 11:10 am - 11:20 am Contributed talk (Annie S Chen ) - "You Only Live Once: Single-Life Reinforcement Learning" 11:20 am - 1:20 pm Mentorship roundtables & Lunch - Mentors: Adam Roberts, Stephanie Hyland, Bianca Zadrozny, Sima Behpour, Mercy Asiedu, Franziska Boenisch, Eleni Triantafillou, Isabela Albuquerque, Yisong Yue, Amy Zhang, Zelda Mariet, Tristan Naumann, Danielle Belgrave, Shakir Mohamed, Tong Sun, Gintare Karolina Dziugaite, Samy Bengio, Rianne van den Berg, Maja Rudolph, Luisa Cutillo, Ioana Bica, Clara Hu, Rosanne Liu, Jennifer Wei, Alice Oh, SueYeon Chung, Erin Grant, Sasha Luccioni, Michela Paganini, Mounia Lalmas-Roelke, Claire Vernade, Alekh Agarwal, Neema Mduma, Vinod Prabhakaran, Savannah Thais, Jonathan Frankle, Ce Zhang, Rose Yu, Jessica Schrouff, Bo Li, Katherine Heller, Ben Poole, Setareh Ariafar, Christina Pavlopoulou, Isabel Morlidge, Kavya Srinet, Cheng Zhang, Elise van der Pol, Diana Montanes, Lise Diagne, Le Yu, Megan Forrester. Afternoon Session 1:20 pm - 1:55 pm Invited talk - Bianca Zadrozny - "Machine Learning for Climate Risk" 1:55 pm - 2:05 pm Contributed talk (Elizabeth Bondi-Kelly) - "Human-AI Interaction in Selective Prediction Systems" 2:05 pm - 2:15 pm Contributed talk (Gowthami Somepalli) - "Investigating Reproducibility from the Decision Boundary Perspective." 2:15 pm - 2:35 pm Coffee break 2:35 pm - 3:10 pm Invited talk -Hima Lakkaraju - "A Brief History of Explainable AI: From Simple Rules to Large Pretrained Models" 3:10 pm - 4:10 pm Panel discussi on 4:10 pm - 4:20 pm Closing Remarks 4:20 pm - 4:30 pm Poster setup 4:30 pm - 6:00 pm Joint Affinity Groups Poster Session Monday, December 5, 2022 [virtual] (Time in ET) 9:30 am - 9:40 am Opening Remarks 9:40 am - 9:55 am Contributed talk (Okechinyere J Achilonu ) - "Natural language processing for automated information extraction of cancer parameters from free-text pathology reports" 9:55 am - 10:10 am Contributed talk (Paula Harder ) - "Physics-Constrained Deep Learning for Climate Downscaling" 10:10 am - 10:25 am Contributed talk (Silvia Tulli ) - "Explanation-Guided Learning for Human-AI collaboration" 10:25 am - 10:40 am Contributed talk (Mina Ghadimi Atigh ) - "Hyperbolic Image Segmentation" 10:40 am - 10:50 am Set up (for mentorship session) 10:50 am - 11:50 am Mentorship Panel (Discussion + Q&A) with Jenn Wortman Vaughan (Microsoft Research), Colin Raffel (University of North Carolina) Kristen Grauman (University of Texas at Austin) 11:50 am - 12:00 pm Break 12:00 pm - 12:35 pm Sponsor Talks 2:00 pm - 4:00 pm Joint Affinity Groups Poster Session Alice Oh KAIST University Hima Lakkaraju Harvard University Bianca Zadrozny IBM Research Raesetje Sefala Distributed AI Research Institute Rianne van den Berg Microsoft Research AI and Creativity: Adam Roberts (Google Brain) Choosing between Academia and Industry: Stephanie Hyland (Microsoft Research) and Bianca Zadrozny (IBM Research) Continual Learning & Open-World Learning: Sima Behpour (Bosch) Founding and Funding Startups: Mercy Asiedu (Google) Gender-related challenges: Franziska Boenisch (Vector Institute) Generalization & Robustness: Eleni Triantafillou (Google Brain) and Isabela Albuquerque (DeepMind) Getting a job (academia): Yisong Yue (Caltech) and Amy Zhang (UT Austin) Getting a job (industry): Zelda Mariet (Google) Healthcare/clinical applications: Danielle Belgrave (DeepMind) and Tristan Naumann (Microsoft Research) Leadership: Shakir Mohamed (DeepMind) and Tong Sun (Adobe) Learning theory: Karolina Dziguaite (Google Brain) Life in industry research: Samy Bengio (Apple) and Rianne van den Berg (Microsoft Research) Life with kids: Maja Rudolph (BCAI) and Luisa Cutillo (University of Leeds) Mental health & surviving in grad school: Ioana Bica (DeepMind), Clara Hu (Google Brain), and Rosanne Liu (Google Brain) ML for Science: Jennifer Wei (Google) Natural language processing: Alice Oh (KAIST) Negotations in ML: Nicole Bannon (81cents) Neuroscience & cognitive science: Erin Grant (UCL), SueYeon Chung (NYU/Flatiron Institute), and Noga Zaslavsky Non-traditional paths in machine learning: Sasha Luccioni (HuggingFace) and Michela Paganini (DeepMind) Recommender systems: Mounia Lalmas-Roelke (Spotify) Reinforcement learning: Claire Vernade (DeepMind), Alekh Agarwal (Google), and Elise van der Pol (Microsoft Research) Seeking funding in academia: Neema Mduma (The Nelson Mandela African Institution of Science and Technology) Social science applications: Vinod Prabhakaran (Google Research), Savannah Thais (Columbia University), and Sarah Brown (University of Rhode Island) Systems and machine learning: Jonathan Frankle (Harvard University/MosaicML) and Ce Zhang (ETH Zurich) Time Series: Rose Yu (UCSD) Trustworthy machine learning: Jessica Schrouff (DeepMind), Bo Li (UIUC), and Katherine Heller (Google Research) ExoMiner: A Highly Accurate and Explainable Deep Learning Classifier That Validates 301 New Exoplanets Noa Lubin (Bar-Ilan University)* When are Local Queries Useful for Robust Learning? Pascale Gourdeau (University of Oxford)*; Varun Kanade (University of Oxford); Marta Kwiatkowska (Oxford University); James Worrell (University of Oxford) Natural language processing for automated information extraction of cancer parameters from free-text pathology reports Okechinyere J Achilonu (University of the Witwatersrand)* Discriminative Candidate Selection for Image Inpainting Lucia Cipolina-Kun (University of Bristol)*; Simone Caenazzo (Riskcare); Sergio Manuel S Papadakis (Konfío) Determination of Neural Network Parameters for Path Loss Prediction in Very High Frequency Wireless Channel Abigail O Jefia (Cisco Systems)*; Segun Popoola (Manchester Metropolitan University); Aderemi A. Atayero (Covenant University) Development of predictive model for survival of paediatric HIV/AIDS patients in south western Nigeria using data mining techniques Agbelusi Olutola (Rufus Giwa Polytechnic )* Modelling non-reinforced preferences using selective attention Noor Sajid (University College London)*; Panagiotis Tigas (Oxford University); Zafeirios Fountas (Huawei Technologies); Qinghai Guo (Huawei Technologies); Alexey Zakharov (Huawei Technologies); Lancelot Da Costa (Imperial College London) Data Analysis and Machine Learning for Speech Music Playlist Generation Maikey Zaki Khorani (Salahaddin University/College of Engineering)* Meta Optimal Transport Brandon Amos (Facebook AI Research); Samuel Cohen (University College London); Giulia Luise (University College London)*; Ievgen Redko (Aalto University) Single-modality and joint fusion deep learning for diabetic retinopathy diagnosis Sara EL-ATEIF (ENSIAS)*; Ali Idri (University Mohamed V) Multi-Armed Bandit Problem with Temporally-Partitioned Rewards Giulia Romano (Politecnico di Milano)*; Andrea Agostini (Politecnico di Milano); Francesco Trovò (Politecnico di Milano); Nicola Gatti (Politecnico di Milano); Marcello Restelli (Politecnico di Milano) The use of Region-based Convolutional Neural Network Model for Analysing Unmanned Aerial Vehicle Remote Sensing Odunayo E Oduntan (Chrisland University Abeokuta, Nigeria)* Kernel Density Bayesian Inverse Reinforcement Learning Aishwarya Mandyam (Stanford University)*; Didong Li (University of North Carolina); Diana Cai (Princeton University); Andrew Jones (Princeton University Department of Computer Science); Barbara Engelhardt (Stanford University) Multimodal Checklists for Fair Clinical Decision Support Qixuan Jin (Massachusetts Institute of Technology)*; Marzyeh Ghassemi (University of Toronto) Investigating Reproducibility from the Decision Boundary Perspective Gowthami Somepalli (University of Maryland, College Park)*; Arpit Bansal (University of Maryland - College Park); Liam Fowl (University of Maryland); Ping-yeh Chiang (University of Maryland, College Park); Yehuda Dar (Rice University); Richard Baraniuk (Rice University); Micah Goldblum (University of Maryland); Tom Goldstein (University of Maryland, College Park) Development of a modified likelihood ratio model for handwriting identification in forensic science Adeyinka O Abiodun (National Open University of Nigeria)*; Sesan Adeyemo (University of Ibadan); Adegboyega Adebayo (National Open University of Nigeria) Self-Supervised Graph Representation Learning for chip design-partitioning on multi-FPGA platforms Divyasree Tummalapalli (Intel Corporation)*; Chiranjeevi Kunapareddy (Intel Corporation); Vikas Akalwadi (Intel Corporation); Rahul Govindan (Intel Corporation); Balaji G (Intel Corporation) Hierarchically Clustered PCA and CCA via a Convex Clustering Penalty Amanda M Buch (Weill Cornell Medicine, Cornell University)*; Conor Liston (Weill Cornell Medicine, Cornell University); Logan Grosenick (Weill Cornell Medicine, Cornell University) Deep Metric Learning to predict cardiac pressure with ECG Hyewon Jeong (MIT)*; Marzyeh Ghassemi (University of Toronto, Vector Institute); Collin Stultz (MIT) Spatial clustering with random partitions on ovarian cancer data Yunshan Duan (University of Texas at Austin)*; Peter Mueller (University of Texas); Wenyi Wang (MD Anderson); Shuai Guo (MD Anderson) Object Segmentation of Cluttered Airborne LiDAR Point Clouds Mariona Carós (Universitat de Barcelona)*; Ariadna Just (Institut Cartogràfic i Geològic de Catalunya); Santi Seguí (Universitat de Barcelona); Jordi Vitria (Universitat de Barcelona) Identifying Disparities in Sepsis Treatment using Inverse Reinforcement Learning Hyewon Jeong (MIT)*; Taylor W Killian (University of Toronto, Vector Institute); Sanjat Kanjilal (Harvard Medical School); Siddharth Nagar Nayak (Massachusetts Institute of Technology); Marzyeh Ghassemi (University of Toronto, Vector Institute) Multi Mix Mask – RCNN (M3RCNN) for Instance Intervertebral Disc Segmentation Malinda Vania (Ulsan National Institute of Science and Technology)*; Lim Sunghoon (Ulsan National Institute of Science and Technology) The Lean Data Scientist: Recent Advances towards Overcoming the Data Bottleneck Chen Shani (The Hebrew university of Jerusalem)*; Jonathan Zarecki (Bar-Ilan University); Dafna Shahaf (The Hebrew University of Jerusalem) Towards an automatic classification for software requirements written in Spanish María Isabel Limaylla Lunarejo (Universidade da Coruña)* Exploiting Pretrained Biochemical Language Models for Targeted Drug Design Gökçe Uludoğan (Bogazici University)*; Arzucan Özgür (Bogazici University); Elif Ozkirimli (Roche AG); Kutlu Ülgen (Bogazici University ); Nilgün Karalı (Istanbul University) Curriculum learning for improved femur fracture classification: scheduling data with prior knowledge and uncertainty Amelia Jiménez-Sánchez (IT University of Copenhagen)*; Diana Mateus (Centrale Nantes); Sonja Kirchhoff (TUM school of medicine); Chlodwig Kirchhoff (TUM school of medicine); Peter Biberthaler (TUM school of medicine); Nassir Navab ("TU Munich, Germany"); Miguel Angel González Ballester (Universitat Pompeu Fabra); Gemma Piella (Pompeu Fabra University) Shortcuts in Public Medical Image Datasets Amelia Jiménez-Sánchez (IT University of Copenhagen)*; Andreas Skovdal (ITU); Frederik Bechmann Faarup (ITU); Kasper Thorhauge Grønbek (ITU); Veronika Cheplygina (ITU) Adaptively Identifying Patient Populations With Treatment Benefit in Clinical Trials Alicia Curth (University of Cambridge)*; Alihan Hüyük (University of Cambridge); Mihaela van der Schaar (University of Cambridge) Improving Robustness to Distribution Shift with Methods from Differential Privacy Neha Hulkund (MIT)* Quantifying Gender Bias in Hindi Language Models Neeraja Kirtane (Manipal Institute of Technology)*; V Manushree (Manipal Institute Of Technology); Aditya Kane (Pune Institute of Computer Technology) De novo PROTAC design using graph-based deep generative models Divya V Nori (Massachusetts Institute of Technology)*; Connor Coley (MIT); Rocio Mercado (Massachusetts Institute of Technology) Self-Contained Entity Discovery from Captioned Videos Melika ayoughi (university of amsterdam)*; Paul Groth (University of Amsterdam); Pascal Mettes (University of Amsterdam) Reduce False Negative in Distant supervised learning using Dependency tree-LSTM to Construct a Knowledge Graph Samira Korani (NUIG)*, John McCrae (NUIG) Break the bottleneck of AI deployment at the edge. Paula Ramos (Intel)*; Helena Kloosterman (Intel); Samet Akcay (Intel); Yu-Chun Liu (Intel Corp.); Raymond Lo (Intel) Towards probabilistic end-to-end Deep Learning Weather Forecasting: Spatio-Temporal Temperature Forecasting using Normalizing Flows Christina Winkler (Technical University of Munich)* Bias Assessment of Text-to-Image Models Sasha Luccioni (Mila)*; Clementine Fourrier (Hugging Face); Nathan Lambert (Hugging Face); Unso Eun Seo Jo (Hugging Face); Irene Solaiman (Hugging Face); Helen Ngo (Hugging Face); Nazneen Rajani (Hugging Face); Giada Pistilli (Hugging Face); Yacine Jernite (Hugging Face); Margaret Mitchell (Hugging Face) SO(3) Equivariant Framework for Spatial Networks Sarp Aykent (Auburn University); Tian Xia (Auburn University)* Protein Structure Ranking with Atom-level Geometric Representation Learning Tian Xia (Auburn University)*; Sarp Aykent (Auburn University) Detecting Synthetic Opioids with NQR Spectroscopy and Complex-Valued Signal Denoising Amber J Day (Los Alamos National Laboratory)*; Natalie Klein (Los Alamos National Laboratory); Michael Malone (Los Alamos National Laboratory); Harris Mason (Los Alamos National Laboratory); Sinead A Williamson (UT Austin) Identifying fine climatic parameters for high maize yield using pattern mining: case study from Benin (West Africa) Souand Peace Gloria TAHI (University of Abomey-Calavi)*; Vinasetan Ratheil Houndji (Institut de Formation et de Recherche en Informatique, University of Abomey-Calavi); Castro Hounmenou (Laboratoire de Biomathématiques et d’Estimations Foresti`eres, Faculty of Agronomic Sciences, University of Abomey-Calavi); Romain Glèlè Kakaï (Laboratoire de Biomathématiques et d’Estimations Foresti`eres, Faculty of Agronomic Sciences, University of Abomey-Calavi) GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks Kenza Amara (ETH Zurich)*; Rex Ying (Yale University); Ce Zhang (ETH) Using Visual Similarity to Navigate eCommerce Inventory Shubhangi Tandon (ebay inc)*; Christopher Miller (ebay inc); Selcuk Kopru (ebay); Senthilkumar Gopal (ebay inc) Mixture of Gaussian Processes with Probabilistic Circuits for Multi-Output Regression Mingye Zhu (University of Science and Technology of China)*; Zhongjie Yu (TU Darmstadt); Martin Trapp (Aalto University ); Arseny Skryagin (TU Darmstadt); Kristian Kersting (TU Darmstadt) Synthetic Data Augmentation for Time Series Forecasting Kasumi Ohno (Toyota Technological Institute)*; Kohei Makino (Toyota Technological Institute ); Makoto Miwa (Toyota Technological Institute); Yutaka Sasaki (Toyota Technological Institute) Task-conditioned modelling of drug-target interactions Emma Petersson Svensson (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria)*; Pieter-Jan Hoedt (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); Sepp Hochreiter (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); Guenter Klambauer (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria) Can deep learning models understand natural language descriptions of patient symptoms following cataract surgery? Mohita Chowdhury (Ufonia Limited)*; Oliver Gardiner (Ufonia Limited); Ernest Lim (Ufonia Limited); Aisling Higham (Ufonia Limited); Nick de Pennington (Ufonia Limited) Hyperbolic Image Segmentation Mina Ghadimi Atigh (University of Amsterdam)*; Julian M Schoep (Promaton); Erman Acar (Vrije Universiteit Amsterdam); Nanne van Noord (University of Amsterdam); Pascal Mettes (University of Amsterdam) Maximum Class Separation as Inductive Bias in One Matrix Tejaswi Kasarla (University of Amsterdam)*; Gertjan Burghouts (TNO); Max van Spengler (University of Amsterdam); Elise van der Pol (Microsoft Research); Rita Cucchiara (Università di Modena e Reggio Emilia); Pascal Mettes (University of Amsterdam) CLOOME: Contrastive Learning for Molecule Representation with Microscopy Images and Chemical Structures Ana Sanchez-Fernandez (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria)*; Elisabeth Rumetshofer (JKU Linz); Sepp Hochreiter (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); Guenter Klambauer (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria) Boosting Multi-modal Contrastive Learning with Modern Hopfield Networks and InfoLOOB Andreas Fürst (JKU Linz); Elisabeth Rumetshofer (JKU Linz)*; Johannes Lehner (Johannes Kepler University); Viet T. Tran (Johannes Kepler University Linz); Fei Tang (Here Technologies); Hubert Ramsauer (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); David Kreil (Institute of Advanced Research in Artificial Intelligence (IARAI) GmbH); Michael K Kopp (Institute of Advanced Research in Artificial Intelligence (IARAI) GmbH); Guenter Klambauer (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); Angela Bitto-Nemling (JKU); Sepp Hochreiter (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria) Global-based Deep Q-Network for Molecule Generation Asmaa Rassil (Faculty of science, University Chouaib Doukkali)*; Hiba Chougrad (University Sidi Mohamed Ben Abdellah); Hamid Zouaki (university Chouaib Doukkali) A Semantically Conditioned Code-Mixed Natural Language Generation for Task-Oriented Dialog Suman Dowlagar (International Institute of Information Technology-Hyderabad)*; Radhika Mamidi (IIIT Hyderabad) Unified Autoencoder with Task Embeddings for Multi-Task Learning in Renewable Power Forecasting Chandana Priya Nivarthi (University of Kassel)*; Stephan Vogt (University of Kassel); Bernhard Sick (University of Kassel) Modern Hopfield Networks for Iterative Learning on Tabular Data Bernhard Schäfl (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); Lukas Gruber (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); Angela Bitto-Nemling (JKU)*; Sepp Hochreiter (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria) Delirium Prediction using Long Short-Term Memory (LSTM) in the Electronic Health Record Siru Liu (Vanderbilt University Medical Center)*; Joseph Schlesinger (Department of Anesthesiology, Division of Critical Care Medicine, Vanderbilt University Medical Center); Allison McCoy (Department of Biomedical Informatics, Vanderbilt University Medical Center); Thomas Reese (Department of Biomedical Informatics, Vanderbilt University Medical Center); Bryan Steitz (Department of Biomedical Informatics, Vanderbilt University Medical Center); Elise Russo (Department of Biomedical Informatics, Vanderbilt University Medical Center); Adam Wright (Department of Biomedical Informatics, Vanderbilt University Medical Center) Evaluating and Improving Robustness of Self-Supervised Representations to Spurious Correlations Kimia Hamidieh (University of Toronto, Vector Institute)*; Haoran Zhang (MIT); Marzyeh Ghassemi (University of Toronto, Vector Institute) FACTORS INFLUENCING POSTGRADUATE STUDENTS' ACADEMIC PERFORMANCE: MACHINE LEARNING APPROACH. Ayodele Esther Awokoya (University of ibadan)* A Simple Phoneme-based Error Simulator for ASR Error Correction Mohita Chowdhury (Ufonia Limited)*; Oliver Gardiner (Ufonia Limited); Yishu Miao (Ufonia Limited) Deep Learning methods for biotic and abiotic stresses detection in fruits and vegetables: state of the art and perspectives Ariane Houetohossou (University of Abomey-Calavi)*; Ratheil Vinasetan HOUNDJI (2Institut de Formation et de Recherche en Informatique, University of University of Abomey-Calavi); Castro Gbêmêmali HOUNMENOU (Laboratoire de Biomathématiques et d’Estimations Forestières, Faculty of Agronomic Sciences, University of Abomey-Calavi); Rachidatou SIKIROU (Laboratoire de Défense des Cultures, Centre de Recherches Agricoles d’Agonkanmey, Institut National des Recherches Agricoles du Bénin (INRAB)); Romain Lucas GLELE KAKAÏ (Laboratoire de Biomathématiques et d’Estimations Forestières, Faculty of Agronomic Sciences, University of Abomey-Calavi) Follow the Flow: An Affective Computing Interface for the On-Line Detection of Flow Mental State Elena Sajno (Università di Pisa)*; G. Riva (Catholic University of Milan, Italy); Nicole Novielli (University of Bari) Under-Counted Tensor Completion with Neural Network-based Side Information Learner Shahana Ibrahim (Oregon State University)*; Xiao Fu (Oregon State University); Rebecca Hutchinson (Oregon State University); Eugene Seo (Brown University) Detecting State Changes in Dynamic Neuronal Networks Yiwei Gong (UT Austin)*; Sinead A Williamson (UT Austin) Learning to Defer in Ranking Systems Aparna Balagopalan (MIT)*; Haoran Zhang (MIT); Elizabeth Bondi-Kelly (MIT); Thomas Hartvigsen (MIT); Marzyeh Ghassemi (University of Toronto, Vector Institute) Categorizing Online Harassment on Twitter using Graph Convolutional Networks Mozhgan Saeidi (Stanford University)* A Domain-Oblivious Approach for Learning Concise Representations of Filtered Topological Spaces for Clustering Yu Qin (Tulane University)*; Brittany Fasy (Montana University); Carola Wenk (Tulane University); Summa Brian (Tulane University) A Recommendation System in Task-Oriented Doctor-Patient Interactions Suman Dowlagar (International Institute of Information Technology-Hyderabad)*; Radhika Mamidi (IIIT Hyderabad) Explaining Predictive Uncertainty by Looking Back at Model Explanations Hanjie Chen (University of Virginia)*; Wanyu Du (University of Virginia); Yangfeng Ji (University of Virginia) DaME: Data Mapping Engine for Financial Services Shubhi Asthana (IBM Research - Almaden)*; Ruchi Mahindru (IBM Watson Research Center) Improved Text Classification via Test-Time Augmentation Helen Lu (Massachusetts Institute of Technology (MIT))*; Divya Shanmugam (MIT); Harini Suresh (MIT); John Guttag (MIT) Model Interpretation based Sample Selection in Large-Scale Conversational Assistants Kiana Hajebi (Amazon Alexa AI)* Human-AI Interaction in Selective Prediction Systems Elizabeth Bondi-Kelly (MIT)*; RAPHAEL KOSTER (DeepMind); Hannah Sheahan (DeepMind); Martin Chadwick (DeepMInd); Yoram Bachrach; Taylan Cemgil (DeepMind); Ulrich Paquet (DeepMind); Krishnamurthy Dvijotham (DeepMind) User-interactive, On-demand Cycle-GAN-Based Super Resolution and Focus Recovery on Whole Slide Images (WSI) Huimin Zhuge (Tulane University)*; Summa Brian (Tulane University); J.Quincy Brown (Tulane University) Self Supervised Learning in Microscopy Aastha Jhunjhunwala (NVIDIA)*; Siddha Ganju (Nvidia) Machine Learning for the detection of diabetic retinopathy Francisca O Oladipo (Thomas Adewumi University)*; Taiwo Amusan (Federal University Lokoja) Topic: Building Identification In Aerial Imagery using Deep learning Proscovia Nakiranda (Stellenbosch University)* Dynamic Head Pruning in Transformers Prisha Satwani (The London School of Economics and Political Science )*; yiren zhao (University of Cambridge); Vidhi Lalchand (University of Cambridge ); Robert Mullins (University of Cambridge) Mobile-PDC: High-Accuracy Plant Disease Classification for Mobile Devices. Samiiha Nalwooga (Stockholm University)*; Henry Mutegeki (Makerere University ) CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks Sakshi Varshney (IIT Hyderabad)*; Vinay K Verma (IIT Kanpur); Srijith PK (IIT, Hyderabad, India); Piyush Rai (IIT Kanpur); Lawrence Carin Duke (CS) Modeling Sharing Time Of Fake And Real News Maya Zeng (Boise State University)*; Cooper Doe (Colorado College); Vladimir Knezevic (City College of San Francisco); Francesca Spezzano (Boise State University); Liljana Babinkostova (Boise State University) Interaction Classification with Key Actor Detection in Multi-Person Sport Videos Farzaneh Askari (University of McGill)*; Rohit Ramaprasad (Birla Institute of Technology and Science); James Clark (McGill University); Martin Levine (McGill University) Estimating Fairness in the Absence of Ground-Truth Labels Michelle Bao (Stanford University)*; Jessica Dai (UC Berkeley); Keegan Hines (Arthur AI); John Dickerson (Arthur AI) Motor Imagery ECoG Signal Classification With Optimal Selection Of Minimum Electrodes Tuga Abdelkarim Ahmed (nile center for technology research)*; Shubham Kumar (.); Ruoqi Huang (.) A Noether's theorem for gradient flow: Continuous symmetries of the architecture and conserved quantities of gradient flow Bo Zhao (University of California, San Diego)*; Iordan Ganev (Radboud University); Robin Walters (Northeastern University); Rose Yu (UC San Diego); Nima Dehmamy (IBM Research) Generating High-Quality Emotion Arcs Using Emotion Lexicons Daniela Teodorescu (University of Alberta)*; Saif Mohammad (National Research Council, Canada) Exposure Fairness in Music Recommendation Rebecca Salganik (Universite de Montreal )*; Fernando Diaz (Google); Golnoosh Farnadi (Mila, HEC Montreal, Université de Montréal) DeepWear: Towards an Automated Textiles Materials Classification using a Taxonomy-based ML Approach Shu Zhong (University College London)*; Miriam Ribul (Royal College of Art); Youngjun Cho (University College London); Marianna Obrist (University College London) Revisiting Graph Neural Network Embeddings Skye Purchase (University of Cambridge)*; yiren zhao (University of Cambridge); Robert Mullins (University of Cambridge) Estimating the Treatment Effect of Antibiotics Exposure on the Risk of Developing Anti-Microbial Resistance Hyewon Jeong (MIT)*; Kexin Yang (Harvard School of Public Health); Ziming Wei (Harvard School of Public Health); Yidan Ma (Harvard School of Public Health); Intae Moon (MIT); Sanjat Kanjilal (Harvard Medical School) Can we explain Aha! moments in artificial agents ? Ikram Chraibi Kaadoud (IMT Atlantique)*; Adrien Bennetot (Segula Technologies - Sorbonne Université - Ensta ParisTech); Barbara Mawhin (Human Factors Department, EBT-Salient Aero Foundation); Vicky Charisi (European Commission, Joint Research Center (JRC)); Natalia Diaz-Rodriguez (Department of Computer Science and Artificial Intelligence, DaSCI Andalusian Institute in Data Science and Computational Intelligence, University of Granada) Multimodal Deep Learning for Weapon Detection Parie R Desai (Marietta High School)*; Prajwal Saokar (Berry College); William Wansing (Mill Creek High School) The Role of Expert-driven Prompt Engineering for Fine-grained Zero-shot Classification in Fashion Dhanashree Balaram (Lily AI)*; Matthew Nokleby (LILY AI); Thiyagarajan Ramanathan (Lily AI); Ajitesh Gupta Gupta (Lily AI); Ravi Kannan (Lily AI) Explaining complex system of multivariate times series behavior Ikram Chraibi Kaadoud (IMT Atlantique)*; Lina Fahed (IMT Atlantique, Lab-STICC); Tian Tian (IMT Atlantique, Lab-STICC); Yannis Haralambous (IMT Atlantique); Philippe Lenca (IMT Atlantique) Computational models of Language Variation in Literary Narratives Krishnapriya Vishnubhotla (University of Toronto)* Graph Transformer Networks for Nuclear Proliferation Detection in Urban Environments Anastasiya Usenko (Pacific Northwest National Laboratory)*; Sameera Horawalavithana (Pacific Northwest National Laboratory); Ellyn Ayton (Pacific Northwest National Laboratory); Joon-Seok Kim (Pacific Northwest National Laboratory); Svitlana Volkova (Pacific Northwest National Laboratory) Automated Staging of Breast Cancer Histopathology Images Using Deep Learning. Angela M Crabtree (Providence Portland Medical Center (Earle A. Chiles Research Institute), University of Oregon)*; Narmada Naik (University Of Montreal); Kevin L Matlock (Omics Data Automation) Gaussian Process parameterized Covariance Kernels for Non-stationary Regression Vidhi Lalchand (University of Cambridge )*; Talay M Cheema (University of Cambridge); Laurence Aitchison (University of Bristol); Carl Edward Rasmussen (Cambridge University) Heart Disease Prediction Using Machine Learning Techniques Asegunloluwa E Babalola (Anchor University, Lagos)*; Tekena Solomon (Anchor University, Lagos) Multi-group Reinforcement Learning for Electrolyte Repletion Promise Osaine Ekpo (Princeton University)*; Barbara Engelhardt (Princeton University) Trading off Utility, Informativeness, and Complexity in Emergent Communication Mycal Tucker (MIT); Julie A. Shah (MIT); Roger Levy (Massachusetts Institute of Technology); Noga Zaslavsky (MIT)* Mitigating Online Grooming with Federated Learning Khaoula Chehbouni (HEC Montreal); Gilles Caporossi (HEC Montreal); Reihaneh Rabbany (McGill University)*; Martine De Cock (University of Washington Tacoma); Golnoosh Farnadi (Mila, HEC Montreal, Université de Montréal) Towards Private and Fair Federated Learning Sikha Pentyala (University of Washington, Tacoma)*; Nicola Neophytou (Mila); anderson nascimento (UW); Martine De Cock (University of Washington Tacoma); Golnoosh Farnadi (Mila, HEC Montreal, Université de Montréal) Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy Rachel Redberg (UC Santa Barbara); Yuqing Zhu (UC Santa Barbara)*; Yu-Xiang Wang (UC Santa Barbara) Characteristics of White Helmets Disinformation vs COVID-19 Misinformation Anika M Halappanavar (Pacific Northwest National Laboratory )*; Maria Glenski (Pacific Northwest National Laboratory) Biomedical Word Sense Disambiguation with Contextualized Representation Learning Mozhgan Saeidi (Stanford University)* Model Understanding and Debugging at The Level of Subpopulation Jun Yuan (New York University)* Fair Active learning by exploiting causal data structure Sindhu C M Gowda (University of Toronto)*; Haoran Zhang (MIT); Marzyeh Ghassemi (University of Toronto) Preference-Aware Constrained Multi-Objective Bayesian Optimization Alaleh Ahmadianshalchi (Washington State University)*; Syrine Belakaria (Washington State university); Janardhan Rao Doppa (Washington State University) Preliminary Study for Impact of Social Media Networks on Traffic Prediction Valeria Laynes Fiascunari (University of Central Florida)*; Luis Rabelo (University of Central Florida) Explanation-Guided Learning for Human-AI collaboration Silvia Tulli (Instituto Superior Técnico)* Trust Me Not: Trust Scoring for Continuous Model Monitoring Nandita Bhaskhar (Stanford University)*; Daniel Rubin (Stanford University); Christopher Lee-Messer (Stanford University) Multispectral Masked Autoencoder for Remote Sensing Representation Learning Yibing Wei (University of Wisconsin - Madison)*; Zhicheng Yang (PAII Inc.); Hang Zhou (PAII, Inc.); Mei Han (PAII Inc.); Pedro Morgado (University of Wisconsin-Madison); Jui-Hsin Lai (PAII Inc.) Learning Pedestrian Behaviour for Autonomous Vehicle Interactions Fanta Camara (University of Leeds)* Comparing neural population responses based on pairwise $p$-Wasserstein distance between topological signatures Liu Zhang (Princeton University)*; Fei Han (National University of Singapore); KELIN XIA (NANYANG TECHNOLOGICAL UNIVERSITY) Adversarial Analysis of Fake News Detectors Annat Koren (City College of San Francisco)*; Hunter Ireland (Boise State University); Sandra D Luo (Timberline High School); Eryn Jagelski-Buchler (Boise State University); Edoardo Serra (Boise State University); Francesca Spezzano (Boise State University) Fast Parameter Tuning for Rule-base Planners towards Human-like Driving Shu Jiang (Apollo Autonomous Driving )*; Szu-Hao Wu (Apollo Autonomous Driving) Model Averaging to Learn Bayesian Network Structures with Non-Linear Structured Representations Charupriya Sharma (University of Waterloo)* Augmenting Driver Decision-Making Using Meta-Inverse Reinforcement Learning Mayuree Binjolkar (University of Washington)*; Yana Sosnovskaya (University of Washington) Explaining black-box models in natural language through fuzzy linguistic summaries - Bipolar Disorder case study Olga Kaminska (Systems Research Institute Polish Academy of Sciences)*; Katarzyna Kaczmarek-Majer (SRI PAS) Physics-Constrained Deep Learning for Climate Downscaling Paula Harder (Fraunhofer ITWM)*; Qidong Yang (New York University); Venkatesh Ramesh (Mila); Prasanna Sattigeri (IBM Research); Alex Hernandez-Garcia (Mila - Quebec AI Institute); Campbell D Watson (IBM Reserch); Daniela Szwarcman (IBM Research); David Rolnick (McGill University, Mila) Graph Convolutional Neural Network-based Quality Assessment of Light Field Images Sana Alamgeer (Universidade de Brasilia, Brazil)* Erased text retrieval from historical palimpsest manuscripts using deep autoregressive priors Anna Starynska (Rochester Institute of Technology)*; David Messinger (Rochester Institute of Technology) Mask R-CNN model for banana diseases segmentation Neema Mduma (The Nelson Mandela African Institution of Science and Technology)*; Christian A Elinisa (The Nelson Mandela African Institution of Science and Technology) Security, IP protection, Privacy on Federated Learning and Machine Learning Edge Devices Mahdieh Grailoo (Tallinn University of Technology)* SOIL MINERAL DEFICIENCY DETECTION USING A DEEP LEARNING ALGORITHM COMMONLY KNOWN AS CONVOLUTIONAL NEURAL NETWORKS JEAN Mrs. AMUKWATSE (UTAMU)* P53 in Ovarian Cancer: Heterogenous Analysis of KeyBERT, BERTopic, PyCaret and LDAs methods Mary Adewunmi (UTAS,Hobart,Australia)*; Richard Oveh (Benson Idahosa University); Christopher Yeboah (PDM University); Solomon A Olorundare (University of Lagos); Ezeobi Peace (Mbarara University of Science and Technology ) Graph-Transformer for Cross-lingual Plagiarism Detection Oumaima Hourrane (University of Hassan II)* Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies Shachi Deshpande (Cornell University)*; Kaiwen Wang (Cornell University); Dhruv Sreenivas (Cornell University); Zheng Li (Cornell University); Volodymyr Kuleshov (Cornell University) A recommendation system for technology intelligence based on multiplex networks Foutse Yuehgoh (African institut for mathematical sciences )* Pre-processing of Social Media Feeds based on Integrated Local Knowledge Base Taiwo Kolajo (Department of Computer Science, Federal University Lokoja, Kogi State, Nigeria)*; Olawande Daramola (CPUT, Cape Town, South Africa ); Ayodele Adebiyi (Department of Computer Science, Landmark University) Attention-Augmented ST-GCN for Efficient Skeleton-based Human Action Recognition Negar Heidari (Aarhus University)*; Alexandros Iosifidis (Aarhus University) Leveraging artificial intelligence for automatic depression detection using speech recognition. Hewitt Tusiime (Makerere University)*; Alvin Nahabwe (Makerere University ); Julius Kimuli (Makerere University); Grace Babirye (Laboremus Uganda) Shapelet Guided Counterfactual Explanation Generation for Black-Box Time Series Classifiers Tina Han (ConsumerAffairs)*; Jette Henderson (CognitiveScale) Weakly Supervised Medical Image Segmentation with Soft Labels and Noise Robust Loss Banafshe BF Felfeliyan (University of Calgary)*; Abhilash Rakkunedeth (University of Alberta); Jacob Jaremko (University of Alberta); Janet Ronsky (University of Calgary) Human trafficking detection using lockstep behaviour methods Maricarmen MA Arenas (Mila)*; reihaneh rabbany (Mila); Golnoosh Farnadi (Mila) Improving Induced Valence Recognition by Integrating Acoustic Sound Semantics in Movies Shreya G Upadhyay (National Tsing Hua University)*; Bo-Hao Su (Department of Electrical Engineering, National Tsing Hua University); Chi-Chun Lee (Department of Electrical Engineering, National Tsing Hua University) Efficient Hospital Management via Length of Stay prediction using Domain Adaptation Lyse Naomi Momo Wamba (KU Leuven)*; Nyalleng Moorosi (Google); Elaine Nsoesie (Boston University); Frank Rademakers (UZ Leuven); Bart DeMoor (KU Leuven) Reinforcement Learning for Cost to Serve Pranavi Pathakota (TCS Research)*; Kunwar Zaid (TCS Research); Hardik Meisheri (TCS Research); Harshad Khadilkar (TCS Research) Dual Channel Training of Large Action Spaces in Reinforcement Learning Pranavi Pathakota (TCS Research)*; Hardik Meisheri (TCS Research); Harshad Khadilkar (TCS Research) Robustness in Weighted Networks Luisa Cutillo (University of Leeds)*; Valeria Policastro (National Research Council); Annamaria Carissimo (National Research Council) Mapping Slums with Machine Learning and Medium-Resolution Satellite Imagery Agatha Mattos (University College Dublin)*; Michela Bertolotto (University College Dublin); Gavin McArdle (University College Dublin) Investigating the Effects of Environmental Factors on the Detection of Laryngeal Cancer from Speech Signals Using Machine Learning Mary L Paterson (University of Leeds)*; Luisa Cutillo (University of Leeds); James Moor (Leeds Teaching Hospitals NHS Trust) 3D-LatentMapper: View Agnostic Single-View Reconstruction of 3D Shapes Alara Dirik (Bogazici University)*; Pinar Yanardag (Bogazici University) Toward Qualitative Mechanical Problem-Solving using Hybrid AI Shreya Bhowmick Banerjee (Rensselaer Polytechnic Institute)*; Selmer Bringsjord (Rensselaer Polytechnic Institute); Naveen Govindarajulu (RPI) Fair Targeted Immunization with Dynamic Influence Maximization Nicola Neophytou (Mila)*; Golnoosh Farnadi (Mila, HEC Montreal, Université de Montréal) You Only Live Once: Single-Life Reinforcement Learning via Learned Reward Shaping Annie S Chen (Stanford University)*; Archit Sharma (); Sergey Levine (UC Berkeley); Chelsea Finn (Stanford) Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time Caroline Choi (Stanford University)*; Huaxiu Yao (Stanford University); Yoonho Lee (Stanford University); Pang Wei Koh (Stanford University); Chelsea Finn (Google) Probabilistic Querying of Continuous-Time Sequential Events Alex J Boyd (UC Irvine); Yuxin Chang (University of California, Irvine)*; Stephan Mandt (University of California, Irivine); Padhraic Smyth (University of California, Irvine) Resume Parsing using an ensemble of CNN, Bi-LSTM and CRF in a Hard Voting Predictive Approach Scholastica N Mallo (Nigeria Defence Academy)*; Francisca Nonyelum Ogwueleka (Department of Computer Science Nigerian Defence Academy Kaduna, Nigeria); Philip Odion (Nigeria Defence Academy); Martin Irhebhude (Nigeria Defence Academy, Kaduna) Transformers for Synthesized Speech Detection Emily Bartusiak (Purdue University)* Polynomials in Bayesian Problems Lilian Wong (Borealis AI)*; Evans Harrell (Georgia Tech) Estimating Uncertainty in Safety-Critical Deep Learning Models Oishi Deb (University of Oxford)* Adaptive Temporal Pattern Matching Sepideh Koohfar (University of New Hampshire)* Respiratory Conditions (EIPH, PLH, and Mucus) in Racehorses Allison B Fisher (Washington State University)*; Warwick Bayly (Washington State University); Sierra Shoemaker (Washington State University); Julia Bagshaw (Washington State University); Yuan Wang (Washington State University); Macarena Sanz (Washington State University) Shared Hardware, Shared Baselines: An Offline Robotics Benchmark Gaoyue Zhou (CMU)*; Victoria Dean (CMU) Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics Noga Mudrik (The Johns Hopkins University)*; Yenho Chen (Georgia Institute of Technology); Eva Yezerets (The Johns Hopkins University); Christopher J Rozell (Georgia Institute of Technology); Adam Charles (Johns Hopkins University) Probabilistic Interactive Segmentation for Medical Images Hallee E Wong (MIT)*; John Guttag (MIT); Adrian V Dalca (MIT) Evaluation of Active Learning and Domain Adaptation on Health Data Kristina Holsapple (University of Delaware)*; Haoran Zhang (MIT); Marzyeh Ghassemi (University of Toronto, Vector Institute) Towards interpretable health monitoring and service anomaly detection in the cloud Yueying Li (Cornell)*; Edward Suh (Cornell University); Christina Delimitrou (Cornell) Hearing Touch: Using Contact Microphones for Robot Manipulation Shaden N Alshammari (Massachusetts Institute of Technology)*; Victoria Dean (CMU); Tess Hellebrekers (Meta AI); Pedro Morgado (University of Wisconsin-Madison); Abhinav Gupta (Carnegie Mellon University Robotics Institute) Adapting the Function Approximation Architecture in Online Reinforcement Learning John D Martin (University of Alberta); Joseph Modayil (DeepMind); Fatima Davelouis Gallardo (University of Alberta)*; Michael Bowling (University of Alberta) FMAM: A novel Factorization Machine based Attention Mechanism for Forecasting Time Series Data Fahim T Azad (Arizona State University)* The WiML Board and Committee would like to thank all the reviewers that helped: Adriana Romero-Soriano (FAIR) Alaa Bessadok (University of Sousse, Tunisia) Amita Misra (IBM) Angelica Aviles-Rivero (University of Cambridge) Ankita Shukla (ASU) Anna Klimovskaia Susmelj (Swiss Data Science Center) Asra Aslam (Insight Centre for Data Analytics, Ireland) Beyza Ermis (Boğaziçi University) Bingshan Hu (University of Alberta) Bo Dong (Amazon) Celestine Mendler-Dünner (Max Planck Institute for Intelligent Systems, Tübingen) Claire Vernade (Deepmind) Dalin Guo (UC San Diego; Twitter, Inc.) Deepika Bablani (IBM Research) Erin Grant (UC Berkeley) Gowthami Somepalli (University of Maryland, College Park) Han Shao (Toyota Technological Institute at Chicago) Hanjie Chen (University of Virginia) Ilke Demir (Intel Corporation) Isabela Albuquerque (DeepMind) Ishita Mediratta (BITS Pilani K.K. Birla Goa Campus) Itir Onal Ertugrul (Utrecht University) Kasturi Bhattacharjee (AWS AI, Amazon) Kavya Gupta (Centralesupelec) Kuan-Ting Chen (National Taiwan University) Maria Glenski (Pacific Northwest National Laboratory) Maria Lomeli (Meta) Mayoore Jaiswal (University of Washington) Mengjiao Wang (Amazon Visual Search) Mina Ghadimi Atigh (University of Amsterdam) Minhae Kwon (Soongsil University) Naga Vara Aparna Akula (CSIR-CSIO) Natalia Efremova (Queen Mary University, London) Nesime Tatbul (Intel Labs and MIT) Niha Beig Case (Western Reserve University) Nora Hollenstein (University of Copenhagen) Obioma Pelka (University of Applied Sciences and Arts Dortmund) Ozge Nilay Yalcin (Simon Fraser University) Pascale Gourdeau (University of Oxford) Peixian Liang (University of Notre Dame) Priyadarshini Kumari (Sony AI) Rania Ibrahim (Purdue University) Sakinat Folorunso Olabisi Onabanjo university Samira Daruki (Expedia Research) Sanae Lotfi (New York University) Sandareka Wickramanayake (National University of Singapore) Sandya mannarswamy (Intel India) Sara Magliacane (University of Amsterdam) Sasha Luccioni (Mila) Shahana Ibrahim (Oregon State University) Shailee Jain (The University of Texas at Austin) Shimeng Peng (Nagoya university) Shinjini Ghosh (Massachusetts Institute of Technology) Shuai Zhang (Amazon) Sinem Aslan (Ca' Foscari University of Venice) Siru Liu (Vanderbilt University Medical Center) Sonali Agarwal (IIIT-Allahabad) Subarna Tripathi (Intel Labs) Surangika Ranathunga (University of Moratuwa) Svitlana Volkova (Pacific Northwest National Laboratory) Syrine Belakaria (Washington State university) Tania Lorido-Botran (Independent Researcher) Utkarshani Jaimini (Artificial Intelligence (AI) Institute- University of South Carolina) Veronika Cheplygina (ITU) Vidhi Lalchand (University of Cambridge) Vishwali Mhasawade (New York University) Weiyan Shi (Columbia University) Xi Rao (ETH) Xiao Zhang (T-Mobile) Xinyi Chen (Google) Xun Tang (Yelp) Yao Qin (University of California, San Diego) Yixin Wang (University of Michigan) Call for Participation The 17th Workshop for Women in Machine Learning (WiML) will be co-located with NeurIPS in New Orleans, Louisiana and will be hybrid. The NeurIPS workshop for Women in Machine Learning will be held in person on Monday November the 28th and virtually on Monday December the 5th with invited speakers, oral presentations, and posters. The event brings together members of the academic and industry research landscape for an opportunity to connect and exchange ideas, and learn from each other. There will be a mentoring session to discuss current research trends and career choices in machine learning. Underrepresented minorities and undergraduates interested in pursuing machine learning research are encouraged to participate. All presenters should be women or non-binary, and all genders are invited to attend. All submissions must abide by the WiML Code of Conduct. Submission page: https://cmt3.research.microsoft.com/WiML2022 Notification of acceptance is now sent to authors. Authors with accepted su bmissions and with a submitted travel funding application will be receiving further communication. IMPORTANT DATES August 1st, 2022 11:59pm PT – Abstract Submission Open on CMT August 26th, 2022 11:59pm PT – Abstract Submission Deadline September 1st, 2022 11:59 PT - Abstract Submission Deadline [extended] September 8th, 2022 11:59pm PT - Travel funding Application Deadline September 15th, 2022 11:59pm PT – Notification of Acceptance September 16th, 2022 11:59pm PT – Notification of Travel Funding November 21st, 2022 11:59pm PT – Registration Deadline for NeurIPS November 28th, 2022 – WiML Workshop Day (in person) December 5th, 2022 – WiML Workshop Day (virtual) SUBMISSION INSTRUCTIONS We strongly encourage students, postdocs, and researchers in all areas of machine learning who are women or non-binary 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 should be woman or non-binary. Submissions will be reviewed in a double-blind setting. Authors of accepted abstracts will be asked to present their work in either a virtual or in-person poster session. A few authors will be selected to give 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 . TRAVEL FUNDING Registration to the NeurIPS conference is required to participate in this year's WiML workshop. Travel funding will be available for eligible WiML participants, to help cover transportation, meals, accomodation, poster printing and/or visa application related costs. The funding amounts will depend on the geographic location of the qualified recipients and their types of needs. Travel funding recipients are required to volunteer during the WiML Workshop. To qualify, the participant must: i) be a woman or non-binary, ii) be the presenting and primary author of an accepted abstract at WiML 2022, and iii) be a student, postdoc, or hold an equivalent position (equivalent positions include unemployed recent grads and early career researchers from underrepresented geographical areas). WiML travel funding is administered as reimbursements after the workshop and no funding is allocated before the workshop. If you are attending NeurIPS, we also encourage you to apply for NeurIPS’ volunteering and travel funding opportunities, which are separate and independent of WiML travel funding. More details can be found on the NeurIPS website . Travel funding application form [now CLOSED]: Authors that have submitted their work will receive the application form starting on September 2, 2022. The application deadline is September 8th, 2022. Applications received past this deadline will not be considered. * Important Note * More information on funding opportunities can be found in our FAQ section of our webpage. AREA CHAIRS Area chairs should be women or non-binary. The role of area chairs is to write a review and suggest an accept/reject decision for each abstract. We expect each area chair to be responsible for up to 10 one-page abstracts. Area chair application form [now CLOSED]: If you are interested in being an area chair, please apply via the application link . Update: the deadline for the AC application has now passed (deadline August 24th). Thank you for your interest. Visa invitation letter Information on visa invitation letter requests can be found in our FAQ section of our webpage. ORGANIZERS Sergul Aydore (Amazon AI) Gloria Namanya (Makerere AI Research) Mariam Arab (Microsoft and Simon Fraser University) Beliz Gunel (Google AI) Kimia Nadjahi (MIT) Konstantina Palla (Spotify Research) Questions? Check out the FAQs or reach us at workshop[at]wimlworkshop[dot]org PLATINUM SPONSORS GOLD SPONSORS SILVER SPONSORS BRONZE SPONSORS SUPPORTERS Committee ORGANIZERS Sergül Aydöre General Chair Konstantina Palla Senior Program Chair Gloria Namanya Finance and Sponsorship Chair Beliz Gunel Mentorship Chair Mariam Arab Logistics Chair Kimia Nadjahi Student Program and Funding Chair Code of Conduct The open exchange of ideas, the freedom of thought and expression, and respectful scientific debate are central to the goals of Women in Machine Learning, Inc. (“WiML”) activities; this requires a community and an environment that recognizes and respects the inherent worth of every person. The purpose of this Code of Conduct (CoC) is to outline expected standards of behavior during WiML activities. Scope This CoC applies to all WiML activities, including but not limited to: Events organized, hosted, co-branded, or in cooperation with WiML Submissions and reviewing processes run by WiML. Communications are sent through communication channels associated with WiML, including but not limited to social media. Meetings and discussions associated with WiML activities. If an activity is in cooperation with another organization, if the other organization has its own CoC, the union of both CoCs apply. Responsibility All attendees, speakers, mentors, panelists, area chairs, reviewers, sponsors, contractors, organizers, volunteers, members of the WiML Board of Directors and Senior Advisory Council (referred to as “Participants” collectively throughout this document) involved in WiML activities as described above are required to comply with this CoC. Reviews should actively avoid subtle discrimination, however inadvertent. In particular, reviewers should avoid comments in reviews about English style or grammar that may be interpreted as implying that the author is “foreign” or “non-native”. Sponsors are equally subject to this CoC. In particular, sponsors should not use images, activities, or other materials that reinforce gender stereotypes or are of a sexual, racial, or otherwise offensive nature at WiML events. Booth staff, including but not limited to volunteers, should not create a sexualized environment. Unacceptable Behavior WiML is dedicated to providing an experience for all participants that is free from harassment, bullying, discrimination, and retaliation. This includes offensive comments related to gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, religion (or lack thereof), politics, technology choices, or any other personal characteristics or considerations made unlawful by federal, state, or local laws, ordinances, or regulations. Inappropriate or unprofessional behavior that interferes with another participant’s full participation will not be tolerated. This includes bullying, intimidation, personal attacks, harassment, sustained disruption of talks or other events, sexual harassment, stalking, following, harassing photography or recording, inappropriate physical contact, unwelcome sexual attention, public vulgar exchanges, derogatory name-calling, or diminutive characterizations, all of which are unwelcome in this community. Advocating for, or encouraging, any of the above behavior, is also considered harassment. No use of images, activities or other materials that are of a sexual, racial, or otherwise offensive nature that may create an inappropriate or toxic environment is permitted. Disorderly, boisterous, or disruptive conduct including but not limited to fighting, coercion, theft, damage to property, or any mistreatment or non-businesslike behavior towards other participants is not tolerated. Scientific misconduct—including but not limited to fabrication, falsification, or plagiarism of paper submissions or research presentations—is prohibited. Reporting If you have concerns related to your participation or interaction at a WiML activity, observe someone else’s difficulties, or have any other concerns you wish to share, you can make a report: Anytime: By email at wiml.code.of.conduct@gmail.com ‬ During an event: In-person to organizers, volunteers, or any member of the WiML Board of Directors. They will then direct you to the designated responder(s) for that event. Organizers and volunteers can be identified by special badges marked as “ORGANIZER” or “VOLUNTEER”. Members of the WiML Board of Directors can be identified by special badges marked as “WiML Board”. There is no deadline by which to make a report. If the person receiving your report is not the designated responder for that event, they will direct you to a designated responder and/or provide you immediate medical or security help and assist you to feel safe for the duration of the activity. Designated responders will follow WiML procedures to respond to and investigate your report. Enforcement Any participant asked by any member of the community to stop any unacceptable behavior is expected to comply immediately. A response of “just joking” will not be accepted; behavior can be harassing without an intent to offend. If a participant engages in behavior that violates this CoC, WiML retains the right to take any action deemed appropriate, including but not limited to: Formal or informal warnings Barring or limiting continued attendance and participation, including but not limited to expulsion from the event Barring from participating in or deriving benefits from future WiML activities Exclusion from WiML opportunities, e.g. leadership, organizing, volunteering, speaking, reviewing, sponsoring, etc. Reporting the incident to the offender’s local institution or funding agencies Reporting the incident to local law enforcement The same actions may be taken toward any individual who engages in retaliation or who knowingly makes a false allegation of harassment. If action is taken, an appeals process will be made available. Investigation Reports of violations will be handled at the discretion of the WiML Board of Directors, who will investigate reports and bring the issue to resolution. Reports made during the activity will be responded to within 24 hours; reports made at other times will be responded to in less than five weeks. All reports will be handled as confidentially as possible and information will be disclosed only as it is necessary to complete the investigation and bring to resolution. There may be situations (such as those involving Title IX issues in the United States and venue- or employer-specific policies) where the member of the WiML Board of Directors informed of the violation will be under an obligation to file a report with another individual or organization outside of WiML. Ongoing Review The WiML Board of Directors welcomes feedback from the community on this CoC policy and procedures; please contact us by email at info@wimlworkshop.org . Acknowledgments This CoC policy was written by adapting the wording and structure from other CoC policies and procedures by Geek Feminism Wiki (created by the Ada Initiative), NeurIPS , ACM , Montreal AI Symposium , and Deep Learning Indaba . 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

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

Empowering Women in Machine Learning: Amplifying Achievements, Elevating Voices, Building Leaders, and Bridging Gaps to enhance the experience of women in machine learning. 10th Annual Workshop for Women in Machine Learning (WiML 2015) Sunday, December 6 Co-Located with NIPS in Palais des Congrès de Montréal, Canada 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 11th year, the 2016 workshop is co-located with NIPS in Barcelona, Spain on December 5, 2016. A History of WiML poster was created to celebrate the 10th workshop , held in 2015 in Montreal, Canada 2015. Besides this un-workshop and annual workshop which is co-located with NeurIPS, Women in Machine Learning also organizes events such as breakfast at ICML and AAAI conferences and local meetup events, maintains a public directory of women active in ML, profiles the research of women in ML, and maintains a list of resources for women working in ML. Invited Speakers Jennifer Chayes Microsoft Research Maya Gupta Google Research Anima Anandkumar Amazon / UC Irvine Education_(42).jpg Suchi Saria John Hopkins Univ Location The workshop takes place in Centre de Convencions Internacional Barcelona , located at Plaça de Willy Brandt, 11-14, 08019 Barcelona, Spain. PROGRAM RESEARCH ROUNDTABLES CAREER & ADVICE ROUNDTABLES POSTERS Sunday, Dec 4 12.00 – 14.00 Registration desk open. Entrance Hall (enter from Entrance C) 14.00 – 19.00 Workshop on Effective Communication by Katherine Gorman of Talking Machines and Amazon (Optional). Invitation-only, RSVP required 16.00 – 18.00 Amazon Panel & Networking (Optional). Invitation-only, RSVP required 17.00 – 19.00 Facebook Lean-In Circles (Optional). Invitation-only, RSVP required 19.15 – 22.00 WiML Dinner (Optional). Separate registration required . Dedicated to Amazon 22.00 – 23.30 OpenAI Happy Hour (Optional). Invitation-only, RSVP required Monday, Dec 5 All events are held in Rooms 111 and 112, level P1, CCIB except for the poster session, which takes place in Area 5+6+7+8, level P0. 07.00 – 08.00 Registration and Breakfast. Dedicated to Microsoft and OpenAI. Registration desk at Entrance Hall (enter from Entrance C); Breakfast in Rooms 111 and 112, level P1 08.00 – 08.05 Opening Remarks 08.05 – 08.40 Invited Talk: Maya Gupta , Google Research. Designing Algorithms for Practical Machine Learning. [Abstract] [Video] 08.40 – 08.55 Contributed Talk: Maithra Raghu, Cornell Univ / Google Brain. On the Expressive Power of Deep Neural Networks. [Abstract] [Video] 08.55 – 09.10 Contributed Talk: Sara Magliacane, VU Univ Amsterdam. Ancestral Causal Inference. [Abstract] [Video] [Slides] 09.10 – 09.15 Break 09.15 – 10.15 Research Roundtables (Coffee served until 9.40am). Dedicated to Apple and Facebook 10.15 – 10.50 Invited Talk: Suchi Saria , John Hopkins Univ. Towards a Reasoning Engine for Individualizing Healthcare. [Abstract] [Video] 10.50 – 11.05 Contributed Talk: Madalina Fiterau, Stanford Univ. Learning Representations from Time Series Data through Contextualized LSTMs. [Abstract] [Video] 11.05 – 11.10 Break 11.10 – 11.25 Contributed Talk: Konstantina Christakopoulou, Univ Minnesota. Towards Conversational Recommender Systems. [Abstract] [Video] [Slides] 11.25 – 12.00 Invited Talk: Anima Anandkumar , Amazon / UC Irvine. Large-Scale Machine Learning through Spectral Methods: Theory & Practice. [Abstract] [Video] [Slides] 12.00 – 13.00 Career & Advice Roundtables 13.00 – 13.30 Lunch and Poster Setup. Dedicated to DeepMind and Google 13.30 – 15.30 Poster Session (Coffee served until 2pm). Open to WiML and NIPS attendees. Dedicated to our Silver Sponsors: Capital One, D.E. Shaw, Intel, Twitter. Area 5+6+7+8, level P0; Round 1: 1.40pm – 2.30pm; Round 2: 2.30pm – 3.20pm; Poster Removal: 3.20pm – 3.30pm 15.30 – 15.45 Raffle and WiML Updates : Tamara Broderick , MIT and Sinead Williamson , UT Austin. [Video] 15.45 – 16.00 Contributed Talk: Amy Zhang, Facebook. Using Convolutional Neural Networks to Estimate Population Density from High Resolution Satellite Images. [Abstract] [Video] 16.00 – 16.35 Invited Talk: Jennifer Chayes , Microsoft Research. Graphons and Machine Learning: Estimation of Sparse Massive Networks. [Abstract] [Video] 16.35 – 16.40 Closing Remarks NIPS Main Conference (NIPS registration required) 17.00 NIPS Opening Remarks. Area 1 + 2, level P0 WiML 2016 Poster Session Monday, Dec 5, 1.30pm to 3:30pm, Area 5+6+7+8, level P0, open to WiML and NIPS attendees 200+ posters covering theory, methodology, and applications of machine learning will be presented in 2 rounds. Accepted posters Accepted posters (with abstracts) . Abstracts listed here are for archival purposes and do not constitute proceedings for this workshop. Information for poster presenters: Posters for both rounds should be setup 1-1.40pm and removed 3.20-3.30pm. Each poster board is shared by 2-3 presenters. Please check the program book for your round number and poster number. Look for that number in the poster room with ‘W’ appended to the front, e.g. W1, W2, etc. Poster size: up to 37.9 inches width and 35.8 inches height (or 96.3 cm x 91.0 cm), portrait or landscape. Table 1: Deep learning I – Katja Hofmann, Microsoft Research, Oriol Vinyals, DeepMind Table 2: Deep learning II – Junli Gu, Tesla, Sergio Guadarrama, Google Research, Niv Sundaram, Intel Table 3: Reinforcement learning – Emma Brunskill, Carnegie Mellon / Stanford, Yisong Yue, Caltech Table 4: Bayesian methods I – Barbara Engelhardt, Princeton, Lamiae Azizi, University of Sydney Table 5: Bayesian methods II – Ferenc Huszar, Twitter / Magic Pony Table 6: Graphical models – Margaret Mitchell, Google Research, Danielle Belgrave, Imperial College London Table 7: Learning theory – Cynthia Rush, Columbia University, Corinna Cortes, Google Research Table 8: Statistical inference and estimation – Katherine M. Kinnaird, Brown University, Alessandra Tosi, Mind Foundry, Oxford Table 9: Optimization – Anima Anandkumar, Amazon / UC Irvine, Puja Das, Apple Table 10: Neuroscience – Irina Higgins, DeepMind, Jascha Sohl-Dickstein, Google Brain Table 11: Robotics – Raia Hadsell, DeepMind, Julie Bernauer, NVIDIA Table 12: Natural language processing I – Catherine Breslin, Amazon, Olivia Buzek, IBM Watson Table 13: Natural language processing II – Pallika Kanani, Oracle Labs, Ana Peleteiro Ramallo, Zalando, Aline Villavicencio, Federal University of Rio Grande do Sul, Brazil Table 14: Healthcare/biology applications – Tania Cerquitelli, Politecnico di Torino, Jennifer Healey, Intel Table 15: Music applications – Luba Elliott, iambicai, Kat Ellis, Amazon Music, Emilia Gomez, Universitat Pompeu Fabra, Barcelona Table 16: Social science applications – Allison Chaney, Princeton University, Isabel Valera, Max Planck Institute for Software Systems Table 17: Fairness, accountability, transparency in machine learning – Sarah Bird, Microsoft, Ekaterina Kochmar, University of Cambridge Table 18: Computational sustainability – Erin LeDell, H2O.ai, Jennifer Dy, Northeastern University Table 19: Computer vision – Judy Hoffman, Stanford University, Manohar Paluri, Facebook Table 20: Human-in-the-Loop Learning – Been Kim, Allen Institute for AI / Univ of Washington, Saleema Amershi, Microsoft Research Table 1: Machine Learning @Amazon: Jumpstarting your career in industry – Anima Anandkumar, Catherine Breslin, Enrica Maria Fillipi Table 2: Careers@Apple – Meriko Borogove, Anh Nguyen Table 3: Machine Learning @DeepMind: Research in industry vs. academia – Nando De Freitas, Viorica Patraucean, Kimberly Stachenfeld Table 4: Machine Learning @Facebook: Sponsorship vs. Mentorship Throughout Your Career – Angela Fan, Amy Zhang, Christy Sauper, Natalia Neverova, Manohar Paluri Table 5: Machine Learning @Google: Industrial Research and Academic Impact – Corinna Cortes, Google Table 6: Machine Learning and Deep Learning @Microsoft – Christopher Bishop, Mir Rosenberg, Anusua Trivedi Table 7: Delivering phenomenal customer experiences with Machine Learning @Capital One – Jennifer Hill, Marcie Apelt Table 8: Networking I – Olivia Buzek, IBM Watson, Jennifer Healey, Intel Table 9: Networking II – Pallika Kanani, Oracle Labs, Been Kim, Allen Institute for AI / Univ of Washington Table 10: Work/Life Balance (academia) – Namrata Vaswani, Iowa State University, Beka Steorts, Duke University Table 11: Work/Life Balance (industry) I – Yuanyuan Pao, Lyft, Antonio Penta, United Technologies Research Centre, Ireland Table 12: Work/Life Balance (industry) II – Kat Ellis, Amazon Music, Puja Das, Apple Table 13: Choosing between academia/industry I – Katherine M. Kinnaird, Brown University, Jascha Sohl-Dickstein, Google Brain Table 14: Choosing between academia/industry II – Sarah Bird, Microsoft, Oriol Vinyals, DeepMind Table 15: Life with Kids – Jenn Wortman Vaughan, Microsoft Research, Julie Bernauer, NVIDIA Table 16: Getting a job (academia) I – Jennifer Chayes, Microsoft Research, Yisong Yue, Caltech Table 17: Getting a job (academia) II – Tamara Broderick, MIT, Cynthia Rush, Columbia University Table 18: Getting a job (industry) I – Anne-Marie Tousch, Criteo, Sergio Guadarrama, Google Research Table 19: Getting a job (industry) II – Margaret Mitchell, Google Research, Erin LeDell, H2O.ai Table 20: Doing a postdoc – Cristina Savin, IST Austria / NYU, Judy Hoffman, Stanford University Table 21: Doing research in industry – Junli Gu, Tesla, Samy Bengio, Google Brain Table 22: Keeping up with academia while in industry – Irina Higgins, DeepMind, Alessandra Tosi, Mind Foundry, Oxford Table 23: Surviving graduate school – Allison Chaney, Princeton University, Viktoriya Krakovna, DeepMind Table 24: Seeking funding: fellowships and grants – Aline Villavicencio, Federal University of Rio Grande do Sul, Brazil, Danielle Belgrave, Imperial College London Table 25: Establishing collaborations – Barbara Engelhardt, Princeton University, Ekaterina Kochmar, University of Cambridge Table 26: Joining startups – Alyssa Frazee, Stripe, Ferenc Huszar, Twitter / Magic Pony Table 27: Scientific communication – Katherine Gorman, Talking Machines, Ana Peleteiro Ramallo, Zalando Table 28: Building your professional brand – Luba Elliott, iambicai, Lamiae Azizi, The University of Sydney Table 29: Commercializing your research – Katherine Boyle, General Catalyst, Zehan Wang, Twitter / Magic Pony Table 30: Long-term career planning – Inmar Givoni, Kindred.ai, Jennifer Dy, Northeastern University Call for Participation The 11th WiML Workshop is co-located with NIPS in Barcelona, Spain on Monday, December 05, 2016. The workshop is a full-day event with invited speakers, oral presentations, and posters. The event brings together faculty, graduate students, and research scientists for an opportunity to connect and exchange ideas. There will also be a panel discussion and a mentoring session to discuss current research trends and career choices in machine learning. Underrepresented minorities and undergraduates interested in pursuing machine learning research are encouraged to participate. While all presenters will be female, all genders are invited to attend. This is a technical workshop with exciting technical talks. Important Dates August 29, 2016 11:59pm PST – Abstract submission deadline September 26, 2016 – Notification of abstract acceptance October 5, 2016 11:59pm PST- Travel grant/oral presentation application deadline October 15, 2016 – End of abstract editing period October 24, 2016 – Notification of travel grant/oral presentation acceptance November 1, 2016 (or before, if we run out of space) – Registration deadline December 4, 2016 – Pre-workshop dinner and events December 5, 2016 – Workshop Submission Instructions We strongly encourage female students, post-docs and researchers in all areas of machine learning to submit an abstract (500 words or less) describing new, previously, or concurrently published research. We welcome abstract submissions in theory, methodology, as well as applications. Authors of accepted abstracts will be asked to present their work in a poster session. A few authors will be selected to give 15 minutes oral presentations. Submission page: https://easychair.org/conferences/?conf=wiml2016 Evaluation criteria: Submissions will be peer reviewed. Abstracts will be evaluated on scientific merit and relevance to the community. To facilitate the peer review process, we encourage authors to sign up as reviewers when submitting abstracts. Examples of accepted abstracts from previous years. Note that despite the option to upload a paper in the submission system, this is not required. Due to the volume of submissions anticipated, we are unable to review any submitted materials besides the requested abstract. Travel Scholarships Registration is free. Partial scholarships will be provided to female students and postdoctoral attendees with accepted abstracts to offset travel costs. GOLD SPONSORS SILVER SPONSORS BRONZE SPONSORS SUPPORTER Committee ORGANIZERS Diana Cai Statistics PhD student University of Chicago Deborah Hanus Computer Science PhD student Harvard University Sarah Tan Statistics PhD student Cornell University Isabel Valera Postdoctoral Fellow Max Planck Institute for Software Systems Rose Yu Computer Science PhD student University of Southern California AREA CHAIRS Danielle Belgrave (Imperial College London) Tamara Broderick (Massachusetts Institute of Technology) Allison Chaney (Princeton University) Deborah Hanus (Harvard University) Pallika Kanani (Oracle Labs) Katherine M. Kinnaird (Brown University) Lizhen Lin (University of Texas at Austin) Maria Lomeli (University of Cambridge) Konstantina Palla (University of Oxford) Sara Wade (University of Warwick) Sinead Williamson (University of Texas at Austin) Svitlana Volkova (Pacific Northwest National Laboratory) 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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