fbpx
  • wiml
  • October 28, 2016

The full program book for WiML 2020 is now available!

Please fill out this form and registered for NeurIPS if you would like to attend. See the logistics page for more information on the various platforms in which the workshop takes place.

We are very excited to welcome the following inspiring invited speakers!

Interpretable AutoML: Powering the machine learning revolution in healthcare in the era of Covid-19 and beyond

Abstract: Medicine stands apart from other areas where AI can be applied. While we have seen advances in other fields with lots of data, it is not the volume of data that makes medicine so hard, it is the challenges arising from extracting actionable information from the complexity of the data. It is these challenges that make medicine the most exciting area for anyone who is really interested in the frontiers of machine learning – giving us real-world problems where the solutions are ones that are societally important and which potentially impact on us all. Think Covid 19!

In this talk I will show how AI and machine learning are transforming medicine and how medicine is driving new advances in machine learning, including new methodologies in automated machine learning, interpretable and explainable machine learning, dynamic forecasting, and causal inference. I will also discuss our experiences in implementing such AI solutions nationally, in the UK, in order to fight the current Covid 19 pandemic as well as how they can be adapted for international use.

Bio:  Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence, and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA. Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Award for Preventative Medicine from the University of Cambridge, an NSF CAREER Award, 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award, 3 ISO Awards, and several best paper awards, including the IEEE Darlington Award. In 2019, she was identified by the National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. Her research expertise spans signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning, and AI. Mihaela’s research focus is on machine learning, AI, and operations research for healthcare and medicine.

Communicating imperfection

Abstract: Public debate around machine learning tends to gravitate between two extremes, from utopia to dystopia. The technology is simultaneously hailed for its superhuman capabilities and denounced for its corrosive impact on civilization. Reality, however, lies in a much less sensational, middle path. In this talk, I posit that we ought to proactively surface imperfections and limitations of AI systems if we are to gain the trust of users and the public in general. I’ll discuss work on AI Explainability that shows how interactive exploration can help people use, interpret, and learn about machine intelligence in empowering ways.

Bio:  Fernanda Viégas is a Principal Scientist at Google, where she co-leads the PAIR  (People+AI Research) initiative, part of Google Brain. She is also an incoming Gordon McKay Professor of Computer Science at Harvard SEAS and Radcliffe Alumnae Professor at the Radcliffe Institute for Advanced Study, where she will be affiliated with the Harvard Business School and expand her collaborations in applied machine learning and data visualization across disciplines such as healthcare and policy. Her work in machine learning, with long-time colleague Martin Wattenberg, focuses on improving human-AI interaction with a broader agenda of democratizing AI technology. She is well known for her contributions to social and collaborative visualization, and the systems she and her team have created are used daily by millions of people. Her visualization-based artwork with Wattenberg has been exhibited worldwide and is part of the permanent collection of the Museum of Modern Art in New York. Fernanda holds a Ph.D. from the MIT Media Lab.

Roles for computing in social justice

Abstract:  In this talk, I will talk about challenges and exciting new opportunities at the intersection of AI and Security, how AI and deep learning can enable better security, and how Security can enable better AI. In particular, I will talk about secure deep learning and challenges and approaches to ensure the integrity of decisions made by deep learning. I will also give an overview on challenges and new techniques to enable privacy-preserving machine learning. Finally, I will conclude with future directions at the intersection of AI and Security.

Bio:  Rediet Abebe is a Junior Fellow at the Harvard Society of Fellows and an incoming Assistant Professor at EECS at UC Berkeley. Abebe holds a Ph.D. in Computer Science from Cornell University, where she was advised by Jon Kleinberg, as well as an M.S. in Applied Mathematics from Harvard University, an M.A. in Mathematics from the University of Cambridge, and a B.A. in Mathematics from Harvard College. Her research is broadly in the fields of artificial intelligence and algorithms, with a focus on equity and justice concerns. As part of this research agenda, she co-founded and co-organizes Mechanism Design for Social Good (MD4SG), a multi-institutional, interdisciplinary research initiative working to improve access to opportunity for historically underserved and disadvantaged communities. This initiative has participants from over 100 institutions in 20 countries and has been supported by organizations including Schmidt Futures, the MacArthur Foundation, and the Institute for New Economic Thinking.

Getting human-robot interaction strategies to emerge from first principles

Abstract:  One of the things for me in robotics is when robots find interesting strategies for how to interact with the world, like navigating closer to a wall to help localization, as solutions to the sequential decision-making problems they are faced with. I want robots to be able to do the same when interacting with people, and much of my work to date has been about formulating HRI problems and modeling/learning enough about people for that to happen. I’d like to use this talk as an opportunity to reflect on some of the things we’ve been able to accomplish so far — cars figure out how to negotiate intersections and merges, robots figure out how to use their physical actions to probe for information, and they automatically become more conservative when they are wrong as a consequence of having the right uncertainty — and on the road ahead.

Bio:  Anca Dragan is an Assistant Professor in EECS at UC Berkeley, where she runs the InterACT Lab. Her goal is to enable robots to work with, around, and in support of people. She works on algorithms that enable robots to a) coordinate with people in shared spaces and b) learn what people want them to do, spanning different applications, from assistive arms to quadrotors, to autonomous cars, and drawing from optimal control, game theory, reinforcement learning, Bayesian inference, and cognitive science. Anca did her Ph.D. in the Robotics Institute at Carnegie Mellon University on legible motion planning. At Berkeley, she helped found and serves on the steering committee of the Berkeley AI Research Lab, is a co-PI for the Center for Human-Compatible AI, and has been honored by the Presidential Early Career Award for Scientists and Engineers (PECASE), the Sloan Fellowship, the NSF CAREER Award, the ONR Young Investigator Award, an Okawa Foundation Research Grant, MIT’s TR35, and an IJCAI Early Career Spotlight.

Schedule

Most activities take place on on Wednesday, December 9. WiML will also participate in the NeurIPS Affinity Groups Joint Poster Session with Black in AI, LatinX in AI, Queer in AI, and Indigenous in AI on Monday, December 7. A listing of posters presented can be found in this Joint Affinity Groups Poster Session program book. On Friday, December 11, WiML will join Black in AI in hosting two speed networking sessions.

Monday, December 7th, 2020

Time (UTC) Event
8:30 pm – 10:30 pm NeurIPS Joint Affinity Groups Poster Session

Wednesday, December 9th, 2020

Time (UTC) Event
Wed Dec 9, 9:40 am – 10:40 am Pre-workshop informal social
10:40 am – 10:50 am Opening remarks – WiML 2020 organizers
10:50 am – 11:00 am WiML D&I Chairs Remarks – Danielle Belgrave and Meire Fortunato
11:00 am – 11:30 am Invited talk – Mihaela van der Schaar
11:30 am – 11:40 am Invited talk Q&A – Mihaela van der Schaar
11:40 am – 11:50 am Contributed talk – Paloma Sodhi, “Learning rich observation models in factor graphs”
11:50 am – 12:00 pm Contributed talk: Liu Leqi, “Rebounding bandits for modeling satiation effects”
12:00 pm – 1:00 pm Poster session #1
1:00 pm – 7:00 pm Sponsor expo
7:00 pm – 7:30 pm Invited talk – Fernanda Viégas
7:30 pm – 7:40 pm Invited talk Q&A – Fernanda Viégas
7:40 pm – 7:50 pm Contributed talk – Nikhila Ravi, “Accelerating 3D deep learning with PyTorch3D”
7:50 pm – 8:00 pm Contributed talk – Sadhika Malladi, “Why language models can help with downstream tasks: A mathematical approach”
8:00 pm – 8:30 pm Break
8:30 pm – 10:00 pm Mentorship roundtables session
10:00 pm – 10:30 pm Invited talk – Rediet Abebe
10:30 pm – 10:40 pm Invited talk Q&A – Rediet Abebe
10:40 pm – 10:50 pm Contributed talk – Michal Moshkovitz, “Unexpected effects of online k-means clustering”
10:50 pm – 11:00 pm Contributed talk – Jessica Dai, “Fairness under partial compliance”
11:00 pm – 12:00 am Poster session #2
12:00 am – 12:30 am Invited talk – Anca Dragan
12:30 am – 12:40 am Invited talk Q&A – Anca Dragan
12:40 am – 1:00 am WiML president closing remarks – Sarah Osentoski
1:00 am – 2:00 am Post-workshop informal social

 

Friday, December 11th, 2020

Time (UTC) Event
4:00 pm – 5:00 pm Joint Speed Networking Session #1 with Black in AI
10:00 pm – 11:00 pm Joint Speed Networking Session #2 with Black in AI

 

Mentorship Roundtables

There are three types of mentorship roundtables: Research roundtables, Career & Life Advice roundtables, and Sponsor roundtables.

Table ID Table topic Table type First mentor Second mentor
1 Deep learning Research Adriana Romero (Facebook / McGill University) Yoshua Bengio (University of Montréal / Mila)
2 Bridging theory and practice of deep learning Research Hanie Sedghi (Google) Percy Liang (Stanford University)
3 Reinforcement learning I Research Mengdi Wang (Princeton University) Marc Bellemare (Google)
4 Reinforcement learning II Research Anna Reali (Universidade de São Paulo) Sham Kakade (University of Washington / Microsoft)
5 Control and online learning Research Ye Pu (University of Melbourne) Elad Hazan (Princeton University)
6 Bayesian methods Research Katherine Heller (Duke University / Google) Trevor Campbell (University of British Columbia)
7 Statistical inference and estimation Research Aarti Singh (Carnegie Mellon University) Benjamin Bloem-Reddy (University of British Columbia)
8 Optimization Research Alina Beygelzimer (Yahoo) Ryan Adams (Princeton University)
9 Healthcare and clinical applications Research Danielle Belgrave (Microsoft) Barbara Engelhardt (Princeton University)
10 Probabilistic graphical models Research Rianne van den Berg (Google) David Blei (Columbia University)
11 Learning theory Research Gintare Karolina Dziugaite (Element AI) John Langford (Microsoft)
12 Meta-learning and AutoML Research Chelsea Finn (Stanford University) Nicolo Fusi (Microsoft)
13 Natural language processing Research Natalie Schluter (Google / IT University of Copenhagen) Colin Raffel (University of North Carolina Chapel Hill / Google)
14 Data-efficient machine learning Research Amy Zhang (McGill University / MILA / Facebook) Marc Deisenroth (University College London)
15 Interpretability and explainability in machine learning Research Finale Doshi-Velez (Harvard Universi Forough Arabshahi (Facebook)
16 Causal inference and counterfactuals Research Yixin Wang (UC Berkeley) Martin Arjovsky (École Normale Supérieure / INRIA)
17 Systems and machine learning Research Virginia Smith (Carnegie Mellon University) Azalia Mirhoseini (Google)
18 Robotics Research Dorsa Sadigh (Stanford University) Bonolo Mathibela (University College London)
19 Computer vision Research Sandra Avila (University of Campinas Institute of Computing) Shuran Song (Columbia University)
20 Human-AI interaction and collaboration Research Luciana Benotti (University of Cordoba / CONICET) Ayanna Howard (Georgia Institute of Technology)
21 Spatiotemporal reasoning Research Rose Yu (University of California San Diego) Jiajun Wu (Stanford University)
22 Fairness, accountability, and ethics in machine learning Research Jenn Wortman Vaughan (Microsoft) Emma Brunskill (Stanford University)
23 Graph-based and combinatorial machine learning Research Jen Neville (Purdue University) Aditya Grover (Facebook)
24 Social science applications Research Emily Denton (Google) Svitlana Volkova (Pacific Northwest National Laboratory)
25 Music applications Research Christine Payne (OpenAI) Oriol Nieto (Pandora/SiriusXM)
26 Machine learning and neuroscience Research Scott Linderman (Stanford University) Ida Momennejad (Microsoft)
27 Deploying machine learning in production Research Paige Bailey (DeepMind) Anusha Ramesh (Google)
28 Open-source software platforms for machine learning Research Aparna Lakshimaran (Facebook) Matt Johnson (Google)
29 Taking on leadership roles (academia + industry) Career & life advice Kate Crawford (New York University / Microsoft / École Normale Supérieure) Nicolas Le Roux (University of Montréal / Mila / Google)
30 Navigating the job search (industry) Career & life advice Stephanie Hyland (Microsoft) Laurent Dinh (Google)
31 Doing research in industry Career & life advice Silvia Chiappa (DeepMind) Samy Bengio (Google)
32 Developing a long-term research plan Career & life advice Olga Russakovsky (Princeton University) Oriol Vinyals (DeepMind)
33 Finding mentors throughout your career Career & life advice Emma Pierson (Microsoft / Cornell Tech) Yasaman Bahri (Google)
34 Navigating academia (job search and tenure application process) Career & life advice Sharon Li (University of Wisconsin-Madison) Daniel Hsu (Columbia University)
35 Choosing between academia and industry Career & life advice Corinna Cortes (Google) Aaron Courville (University of Montréal / Mila)
36 Seeking funding (academia edition): PhD fellowships / professorship grants Career & life advice Soledad Villar (Johns Hopkins University) Sarah Tan (Facebook)
37 Seeking funding: Negotiating compensation in industry / raising capital for startups Career & life advice Sarah Osentoski (Iron Ox) Miriam Huntley (Day Zero Diagnostics)
38 Establishing collaborations Career & life advice Jennifer Healey (Adobe) Danai Koutra (University of Michigan)
39 Work-life balance (academia) Career & life advice Sarah Brown (University of Rhode Island) Yisong Yue (California Institute of Technology)
40 Surviving graduate school Career & life advice Michela Paganini (Facebook) Jessica Thompson (University of Montréal / Mila)
41 Building your professional brand Career & life advice Devi Parikh (Georgia Tech / Facebook) Gautam Kamath (University of Waterloo)
42 Work-life balance (industry) Career & life advice Meire Fortunato (DeepMind) Hugo Larochelle (Google / University of Montréal / Mila)
43 Life with kids Career & life advice Luciana Ferrer (University of Buenos Aires / CONICET) Cheng Soon Ong (Data61 / CSIRO / Australian National University)
44 Putting machine learning research into practice Career & life advice Paroma Varma (Snorkel AI) Lisa Amini (IBM)
45 Scientific communication Career & life advice Po-Ling Loh (University of Wisconsin-Madison) Ferenc Huszár (University of Cambridge)
46 Non-traditional paths to machine learning Career & life advice Jane Wang (DeepMind) Sergio Guadarrama (Google)
47 Doing a postdoc Career & life advice Natasha Jaques (Google / UC Berkeley) Dhanya Sridhar (Columbia University)
48 Doing data science and machine learning in nonprofit organizations Career & life advice Lauren Chambers (ACLU) Rayid Ghani (Carnegie Mellon University)
49 Managing online communication Career & life advice Rachel Thomas (fast.ai / University of San Francisco) Sinead Williamson (University of Texas at Austin)
50 Democratizing ML research: Non-traditional research methods Career & life advice Rosanne Liu (ML Collective) Pablo Samuel Castro (Google)
51 Internships at Apple Sponsor Shaona Ghosh (Apple) Kayleigh Bottini (Apple)
52 Careers @ ASAPP: Machine learning research at startups Sponsor Ramya Ramakrishnan (ASAPP) Ethan Elenberg (ASAPP)
53 Research Careers in RL @ DeepMind Sponsor Feryal Behbahani (DeepMind) Ashley Edwards (DeepMind)
54 NVIDIA AMA: Ask Me Anything Sponsor Shalini De Mello (NVIDIA) Ashley Jetson (NVIDIA)
55 Careers @ Amazon Sponsor Erika Pelaez Coyotl (Amazon) Priya Gupta (Amazon)
56 QuantumBlack’s “Lessons for new data scientists we wish we had known” Sponsor Viktoriia Oliinyk (QuantumBlack) Diana Murgulet (QuantumBlack)
57 Research at Google Sponsor Elaine Le (Google) Cory Silvear (Google)
58 Careers @ ServiceNow Sponsor Roshnee Sharma (ServiceNow) Sumana Ravikrishnan (ServiceNow)
59 NLP @ IBM for Helping Combat COVID-19 Sponsor Yunyao Li (IBM) Mihaela Bornea (IBM)
60 Careers @ Microsoft Sponsor Susan Dumais (Microsoft) Chris Bishop (Microsoft)
61 Internships and Careers at Netflix Sponsor Sui Huang (Netflix) Corey Twitty (Netflix)
62 Careers in AI at Facebook Sponsor Kavya Srinet (Facebook)
63 AI in Fashion @ Zalando Sponsor Ana Peleteiro (Zalando) Nour Karessli (Zalando)

Sponsor Expo

Sponsor talks are playable by participants on-demand. There will also be sponsor booths, staffed at these times.

Time Sponsor Title Speaker
On-Demand DeepMind Women at DeepMind: Applying for Technical Roles Feryal Behbahani, Mihaela Rosca, Kate Parkyn
On-Demand Facebook Learning Generalized Visual Representations at Facebook Deepti Ghadiyaram, Xueting Yan
On-Demand Apple Machine Learning at Apple Karla Vega
On-Demand IBM Research Spotlight on Women at IBM Research Lisa Amini, Francesca Rossi, Celia Cintas, Payel Das
On-Demand Quantum Black QuantumBlack Revolutionising Advanced Industries using Machine Learning Diana Murgulet
On-Demand Google Responsible AI for Healthcare at Google Jessica Schrouff
On-Demand Microsoft Microsoft Inspires Young Women to Pursue Careers in STEM Multiple
On-Demand NVIDIA Research at NVIDIA: New Core AI and Machine Learning Lab Anima Anandkumar
On-Demand ServiceNow Machine Learning at ServiceNow Haleh Tabrizi, Dorit Zilbershot

Socials

The following participant-hosted socials will take place pre/during/post the workshop. We highly encourage WiML participants to attend, to meet fellow participants in a fun and casual setting. See description of each social.

Date Time (UTC) Social Name Hosts
Dec. 9 9:40 am – 10:40 am def mitigate_BIAS ( UNION (your_take , DeepLearning’s_take ) ) Taniya Seth
Dec. 9 9:40 am – 10:40 am Applying to and navigating PhDs Lisa Schut, Limor Gultchin, Luisa Zintgraf
Dec. 9 9:40 am – 10:40 am The role of machine learning systems in critical societal functions: an open conversation Mariya Vasileva
Dec. 9 9:40 am – 10:40 am Are we prepared for what it is coming next? Gissel Velarde
Dec. 9 9:40 am – 10:40 am How to get most out of your time – best practices in time management Mubashara Akhtar, Devi Bhattarai
Dec. 9 1:00 pm – 2:00 pm Learning mysteries: stories of failures in AI Gagana B, Sreetama Sarkar
Dec. 9 1:00 pm – 2:00 pm Research and job opportunities for ML researchers coming from non-traditional career paths Laia Tarres, Francisca Cattan Castillo
Dec. 9 1:00 pm – 2:00 pm Women in intelligent robotics Meenakshi Sarkar, Vaishnavi Agrawal, Prabhu Pradhan, Jasmine Jerry Aloor
Dec. 9 1:00 pm – 2:00 pm Gender equity during the pandemic Luisa Cutillo, Fariba Yousefi
Dec. 9 1:00 pm – 2:00 pm Un-bookclub race after technology Anoush Najarian, Aleshia Hayes, Ishaani, Melanie King-Dollie, Louvere Walker-Hannon, Sindhuja Parimalarangan
Dec. 9 1:00 pm – 2:00 pm Leading the next generation of black women in AI Louvere Walker-Hannon, Eliane Birba, Nwamaka Okafor, Erick Oduniyi, Shanae Chapman, Kate Kallot
Dec. 10 1:00 am – 2:00 am Problem adaptation to machine learning problems // And now for something completely different: cool ideas for ML from unusual places Larissa Schiavo, Sakinat Folorunso
Dec. 10 1:00 am – 2:00 am Improv team-building for scientists! Xiangyu Peng, Siyan Li
Dec. 10 1:00 am – 2:00 am Staying sane (and safe) when everything is virtual Tahiya Chowdhury, Shiran Dudy
Dec. 10 1:00 am – 2:00 am Set your intention: career vision board for the future Nadia Ahmed, Quynh Le

Joint Speed Networking Sessions with Black in AI

These sessions take place on the Glimpse platform. Participants will be randomly paired up with another participant for 5-minute speed networking, and will receive several random pairings over the 1 hour session. We suggest preparing one or two lines to describe your work and research, as well as any other topics you may want to discuss.

Accepted Posters

For poster IDs and poster session locations, see this section of the program book.

Poster Title Authors
VLSI Physical Design Automation Using Deep Reinforcement Learning Indrapriyadarsini Sendilkkumaar (Shizuoka University)*; Shahrzad Mahboubi (Shonan Institute of Technology); Hiroshi Ninomiya (Shonan Institute of Technology); Takeshi Kamio (Hiroshima City University); Hideki Asai (Shizuoka University)
When Evaluating Language Models, Are Some Words Worth More than Others? Shiran Dudy (OHSU)*; Steven Bedrick (OHSU)
Prune Responsibly Michela Paganini (Facebook)*
On Measuring and Mitigating Biased Inferences of Word Representations Sunipa Dev (University of Utah)*
Predicting Response to Bolus Insulin Elizabeth Eby (Eli Lilly and Company); Neal Kelly (Optum); Jeffrey Hertzberg (Optum); Moira Blodgett (Optum)*; Callie Stubbins (Optum); Eric Meadows (Eli Lilly and Company); Brian Benneyworth (Eli Lilly and Company); Douglas Faries (Eli Lilly and Company)
Identifying Climate Change in Financial Disclosures using Question Answering Sasha Luccioni (Mila)*; Emily Baylor (McGill University)
An Empowerment-based Solution to Robotic Manipulation Tasks with Sparse Rewards Siyu Dai (Massachusetts Institute of Technology)*; Wei Xu (Horizon Robotics); Andreas Hofmann (MIT); Brian C Williams (Massachusetts Institute of Technology)
Ensembles for Time Series Forecasting – an Application in Capacity Planning Xiao Zhang (T-Mobile)*; Marie Jacinto (T-Mobile)
Lookahead–Minimax Tatjana Chavdarova (EPFL)*; Matteo Pagliardini (École polytechnique fédérale de Lausanne); Martin Jaggi (EPFL); François Fleuret (University of Geneva)
Comparative Analysis of Machine Learning Classification Techniques for Neonatal Postprandial Hypoglycemia Symptoms Screening. Elizabeth Ndunge Mutua (Dedan Kimathi university of science and technology)*; loiuse Nyakango (The Kenya Medical Training College)
Bandit-Based Learning and Planning for Green Security Lily Xu (Harvard University)*; Elizabeth Bondi (Harvard University); Fei Fang (Carnegie Mellon University); Andrew Perrault (Harvard University); Kai Wang (Harvard University); Milind Tambe (Harvard University)
Stochastic Gradient Descent with Polyak’s learning rate Mariana Oliveira Prazeres (McGill University)*; Adam Oberman (McGill University)
HF and COPD Admissions WiML Abstract Elizabeth Hancock (Optum)*
Incorporating Clinical Notes in a Multimodal Early Sepsis Prediction Model to Improve Prediction Performance Siba Siddique (New York University Abu Dhabi)*; Mariët Theune (University of Twente, The Netherlands); Shenghui Wang (University of Twente); Farah E Shamout (New York University)
Classification Algorithm for Stochastic Resonance Identification in Human Perceptual Thresholds Jamie L Voros (The University of Colorado Boulder)*; Rachel Rise (The University of Colorado Boulder
Fair Online Post-Processing for Black-Box ML Screening Systems Swetasudha Panda (Oracle Labs); Ari Kobren (UMass Amherst)*; Jean-Baptiste Tristan (Boston college); Michael Wick (Oracle Labs)
Wasserstein Measure of Dependence in Multi-sample Nonparametric Bayesian Learning Marta Catalano (Bocconi University)*; Antonio Lijoi (U Bocconi); Igor Pruenster (Bocconi University Milano)
Explaining Neural Matrix Factorization with Gradient Rollback Carolin Lawrence (NEC Laboratories Europe)*; Timo Sztyler (NEC Laboratories Europe GmbH); Mathias Niepert (NEC Laboratories Europe)
Using ML to Visualize the Impacts of Climate Change Alexia Reynaud (Mila, Université de Montréal)*; Mélisande Teng (Mila, Université de Montréal); Sunand Raghupathi (Mila, Université de Montréal); Tianyu Zhang (Mila, Université de Montréal); Victor Schmidt (Mila, Université de Montréal); Sasha Luccioni (Mila)
Scaffolded Meta-Learner for Efficient Teaching of Human Learners Alexandra Moringen (CITEC)*
System Identification Through Lipschitz Regularized Deep Neural Networks Elisa Negrini (Worcester Polytechnic Institute)*; Giovanna Citti (University of Bologna); Luca Capogna (Worcester Polytechnic Institute)
Identifying and mitigating low-quality labels for deep learning in glaucoma Sonia Phene (Google)*; Joy Hsu (Stanford University); Akinori Mitani (Google); Jieying Luo (Google); Naama Hammel (Google); Jon Krause (Google); Rory sayres (Google)
Choreo-Graph: Learning Latent Graph Representations of the Dancing Body with Graph Neural Networks Mariel N Pettee (Yale University)*; Marcel Nassar (Intel Corporation); Santiago Miret (Intel Labs); Somdeb Majumdar (Intel Labs)
Causal and Interpretable Learning for Datacenter Latency Prediction Yi Ding (University of Chicago)*; Avinash Rao (University of Chicago); Henry (Hank) Hoffmann (The University of Chicago)
PCA-based Low-complexity Anomaly Detection for Low-end IoT Devices Hyoseon Kye (Soongsil University)*; Minhae Kwon (Soongsil University)
Spatiotemporal Features Improve Fine-Grained Butterfly Image Classification Marta Skreta (University of Toronto)*; Sasha Luccioni (Mila); David Rolnick (McGill University, Mila)
Probably Efficient Fairness-Aware Policy Optimization for Multi-Agent Reinforcement Learning Jiawei Ge (Fudan University)*; Lingxiao Wang (Northwestern University); Zhuoran Yang (Princeton); Zhaoran Wang (Northwestern U)
Addressing challenges of ICU data: an interpretable machine learning framework for predicting mortality Ghadeer O Ghosheh (New York University Abu Dhabi)*; Farah E Shamout (New York University)
Deep Unfolding Approaches for Sparse Dictionary Learning in Video Summarization Sagnik Mukherjee (IIITMK); Sinnu Thomas (IIITMK)*
Binding affinity prediction: a comparative study of three approaches Sofiya Garkot (Ukrainian Catholic University )*; Dzvenymyra Yarish (SoftServe Inc); Maksym Druchok (SoftServe Inc.); Oleksandr Gurbych (SoftServe)
Fairness Under Partial Compliance Jessica Dai (Brown University)*; Sina Fazelpour (Carnegie Mellon University); Zachary Lipton (Carnegie Mellon University)
SGD in Hilbert Scales: Smoothness, Preconditioning and Earlier Stopping Nicole Muecke (Technical University Berlin)*; Enrico Reiss (University of Potsdam)
Seizure Type Detection using Autoencoder-Based Features Tina Raissi (RWTH Aachen University)*
Learning Hyperbolic Representations for Unsupervised 3D Segmentation Joy Hsu (Stanford University)*; Jeffrey Gu (Stanford University); Serena Yeung (Stanford University)
Self-Training with Improved Regularization for Sample-Efficient Chest X-Ray Classification Deepta Rajan (IBM Research)*; Jayaraman Thiagarajan (Lawrence Livermore National Laboratory); Alexandros Karargyris (IBM); Satyananda Kashyap (IBM Research)
A polynomial-time algorithm for learning nonparametric causal graphs Yi Ding (University of Chicago)*; Bryon Aragam (University of Chicago); Ming Gao (the University of Chicago)
Towards Better Prosthetic Arms with Intelligent Robotics alishba imran (hanson robotics ltd)*
AutoFreeze: Automatically Freezing Transformer Blocks to Accelerate Model Fine-tuning Yuhan Liu (University of Wisconsin-Madison)*; Saurabh Agarwal (UW-Madison); Shivaram Venkataraman (University of Wisconsin, Madison)
TrackRecSign : Tracking and Recognition Based Ensemble Approach for Indian Sign Language Shambhavi Mishra (CBP Government Engineering College)*; Janamejaya Channegowda (Ramaiah Institute of Technology); Abhishek Annamraju (Tessellate Imaging); Jyothi Swaroop Kasina (Indian Institute of Technology (Indian School of Mines) ); Rathna G N (Indian Institute of Science, Bangalore)
AI and Morality: Teaching Human Morals to Machines Frehiwot G Girmay (Aksum university)*; Aneta Neumann (The University of Adelaide)
Personalized Regularization Learning for Fairer Matrix Factorization Sirui Yao (Virginia Tech)*; Bert Huang (Tufts University)
Why Language Models Can Help with Downstream Tasks: A Mathematical Approach Nikunj Saunshi (Princeton University); Sadhika Malladi (Princeton University)*; Sanjeev Arora (Princeton University)
Benchmarking Reverse-Complement Strategies for Deep Learning Models in Genomics Hannah Zhou (Palo Alto Senior High School)*; Avanti Shrikumar (Stanford University); Anshul Kundaje (Stanford University)
Application of Neural Language Models for Research Article Classification into Sustainable Development Goals Daniela I Flores (Pontificia Universidad Católica de Chile)*; Denis Parra (Pontificia Universidad Católica de Chile); Ignacio Oliva (Pontificia Universidad Católica de Chile)
OPEN SOURCE TOOL FOR MALARIA DATASET ANNOTATION Martha Stephen Shaka (The University of Dodoma)*; Nyamos S Waigama (The University of Dodoma); Frederick R Apina (University of Dodoma)
Estimating Ships Time of Arrival based on AIS Data and Artificial Neural Networks: A Case Study Sara El Mekkaoui (EMI Engineering School)*; Loubna Benabou (UQAR); Abdelaziz Berrado (EMI Engineering School)
Analysis on Impact of COVID-19 on Port Activity using Maritime Data and Machine Learning: A Case Study Sara El Mekkaoui (EMI Engineering School)*; Loubna Benabou (UQAR); Abdelaziz Berrado (EMI Engineering School)
Testing Tail Weight of a Distribution Via Hazard Rate Maryam Aliakbarpour (MIT); Amartya Biswas (MIT); Kavya Ravichandran (Massachusetts Institute of Technology)*; Ronitt Rubinfeld (MIT, TAU)
Determining Geometric Priors for Relational Representation Learning Melanie Weber (Princeton University)*
Augmenting Existing Embeddings with Relational Knowledge Emily Mu (MIT)*; Harini Suresh (MIT); John Guttag (MIT)
Diverse dynamical mechanisms underlying working memory function in recurrent networks Elham Ghazizadeh (Washington University in St. Louis)*; ShiNung Ching (Washington University in St. Louis)
Contrastive learning of strong-mixing continuous-time stochastic processes Bingbin Liu (Carnegie Mellon University)*; Pradeep Ravikumar (Carnegie Mellon University); Andrej Risteski (CMU)
Explaining Black-box Ensemble Models for Early Alzheimer’s Disease Classification Louise Bloch (University of Applied Sciences and Arts Dortmund)*; Christoph M. Friedrich (University of Applied Sciences and Arts Dortmund)
Learning the latent distribution in generative models with Entropic Optimal Transport loss Giulia Luise (University College London)*; Massimiliano Pontil (); Carlo Ciliberto (Imperial College London)
Covariance-Based Generative Models Aneta Neumann (The University of Adelaide)*
Event Detection in Social Media Streams Taiwo Kolajo (Department of Computer Science, Federal University Lokoja)*; Olawande Daramola (CPUT, Cape Town, South Africa ); Ayodele Adebiyi (Department of Computer Science, Landmark University)
Invariant Representation Learning for Treatment Effect Estimation Claudia Shi (Columbia University)*; Victor Veitch (Google; University of Chicago); David Blei (Columbia University)
Predicting Student Success Using Student Engagement in Blended-Learning Course Eluwumi F Buraimoh (University of Witwatersrand,Johannesburg)*; Ritesh Ajoodha (Wits University); Kershree Padayachee (University of Witwatersrand,Johannesburg)
Reference-free cell type and phenotype annotation in single cell RNA sequencing by learning representations Soroor Hediyeh-zadeh (The Walter and Eliza Hall Institute of Medical Research)*; Yi Xie (Walter and Eliza Hall Institute of Medical Research); Jean Berthelet (Olivia Newton-John Cancer Research Institute
Hybrid Sequence to Sequence Model for Video Object Segmentation Fatemeh Azimi (TU Kaiserslautern)*; Stanislav Frolov (TU Kaiserslautern); Federico Raue (DFKI); Jörn Hees (DFKI); Andreas Dengel (DFKI GmbH)
Entity and Relation Linking is All You Need for KGQA Sukannya Purkayastha (IIT Kharagpur)*; Saswati Dana (IBM Research); G P Shrivatsa Bhargav (IBM); Dinesh Khandelwal (IBM Research AI); Dinesh Garg (IBM Research AI)
Using weak supervision to generate training datasets from social media data: a proof of concept to identify drug mentions Ramya Tekumalla (Georgia State University)*; Juan M Banda (Georgia State University)
Measuring the Similarity of Arguments With BERT Maike Behrendt (Heinrich-Heine Universität)*; Stefan Harmeling (Heinrich Heine University Düsseldorf)
Robust Life-Long Learning Sima Behpour (University of Pennsylvania)*
Large-Scale Shared Response Model for Latent Feature Analysis Jennifer Hsia (Princeton University)*; Peter J Ramadge (Princeton)
Learning to Limit Data Collection for Data Minimization Compliance Divya Shanmugam (MIT)*; Samira Shabanian (Microsoft Research); Fernando Diaz (Google); Michele Finck (MPI); Asia Biega (Microsoft Research)
Identifying evidence at scale for drug repurposing for cancer Ioana Baldini (IBM Research)*
Detection of Malaria Vector Breeding Habitats using Topographic Models Aishwarya N Jadhav (Veermata Jijabai Technological Institute)*
STRATA: Building Robustness with a Simple Method for Generating Black-box Adversarial Attacks for Models of Code Bryn M Reinstadler (Massachusetts Institute of Technology (MIT))*; Jacob M Springer (Swarthmore College); Una-May O’Reilly (MIT)
Cognitive Diversity to Solve Algorithmic Bias in AI/ML Pipeline Shilpi Agarwal (DataEthics4All)*; Susanna Raj (DataEthics4All); Taurean Dyer (DataEthics4All); Brett Drury (Dataethics4all)
Machine Learning for Automatic Approval of Clinical Discharges to Post-Acute Care Settings Allison M Emeott (Optum)*; Priya Kumari (Optum); Nathan P Swanson (Optum)
HAP-SAP: Semantic Annotation in LBSNs using Latent Spatio-Temporal Hawkes Process Manisha Dubey (Indian Institute of Technology Hyderabad)*; P. K. Srijith (IIT Hyderabad); Maunendra Sankar Desarkar (IIT Hyderabad)
A Deep-Generative Hybrid Model to Integrate Multimodal and Dynamic Connectivity for Predicting Spectrum-Level Deficits in Autism Niharika S. D’Souza (The Johns Hopkins University)*; Mary Beth Nebel (Kennedy Krieger Institute); Deana Crocetti (Kennedy Krieger Institute); Nicholas Wymbs (Kennedy Krieger Institute); Joshua Robinson (Kennedy Krieger Institute); Stewart Mostofsky (Kennedy Krieger Institute); Archana Venkataraman (Johns Hopkins University)
How can we make the content on YouTube safer? Jassimran Kaur (BroadbandTV)*; Ruishen Lyu (BroadbandTV); Giulia Ohashi (BroadbandTV); Farhad Faradji (BroadbandTV); Guillaume Mercey (BroadbandTV); Mehrdad Fatourechi (BroadbandTV)
Parametric Bounds on the Complexity of the Optimal Transport Map Tanya Marwah (Carnegie Mellon University)*; Zachary Lipton (Carnegie Mellon University); Andrej Risteski (CMU)
Unsupervised Graph based Telugu News Articles Text Summarization Vakada Lakshmi Sireesha (IIIT-Hyderabad)*; Charan Chinni (IIIT-Hyderabad); Mounika Marreddy (IIIT Hyderabad); Subba Reddy Oota (IIIT Hyderabad); Radhika Mamidi (IIIT Hyderabad)
Unexpected Effects of Online k-means Clustering Michal Moshkovitz (UC San Diego)*
Integration of Thoracic CT and Brain MR Scans for Survival Prediction of Cancer Immunotherapy Patients Melda Yeghaian (The Netherlands Cancer Institute)*; Stefano Trebeschi (The Netherlands Cancer Institute); Zuhir Bodalal Elkarghali (
Mask Combination of Multi-layer Graphs for Global Structure Inference Eda Bayram (EPFL)*; Dorina Thanou (); Elif Vural (Middle East Technical University)
Deep MIML with Mean Pooling and Attention Mridula Maddukuri (CognitiveScale)*; Sinead Williamson (UT Austin/CognitiveScale)
Fairness-Aware‌ ‌Resource‌ ‌Assignment:‌ ‌A‌ ‌Case‌ ‌Study‌ ‌on‌ ‌Policing‌ Tasfia Mashiat (George Mason University)*; Xavier Gitiaux (George Mason University); Huzefa Rangwala (George Mason University); Sanmay Das (George Mason University)
Gaussian Process Latent Variable Models with Normalising Flows Vidhi Lalchand (University of Cambridge )*; Neil D Lawrence (University of Cambridge)
FACTORS INFLUENCING POSTGRADUATE STUDENTS’ ACADEMIC PERFORMANCE: MACHINE LEARNING APPROACH Ayodele Esther Awokoya (University of ibadan)*
Bias and Variance in Standardized Tests and Affirmative Action for College Admissions Nikhil Garg (Stanford University); Hannah Li (Stanford University); Faidra Monachou (Stanford University)*
Factor-analytic inverse regression for high-dimension, small-sample dimensionality reduction Aditi Jha (Princeton University)*; Jonathan W Pillow (Princeton University); Michael J. Morais (Princeton University)
Detecting Handling Bleed Valve Fault using Machine Learning algorithms Oishi Deb (Queen Mary University of London)*
Perturbation Analysis of Two Deep Learning Algorithms Employed in Cervical Cancer Classification Emmanuella A. W Budu (University of Botswana)*; V Lakshmi Narasimhan (University of Botswana); Zablon Mbero (University of Botswana)
Deep Convolutional Neural Networks for Representing Gene Expression Images of the Human Brain Pegah Abed-Esfahani (CAMH)*; Benjamin Darwin (SickKids); Nick Wang (SickKids); Sean Hill (CAMH, University of Toronto); Jason Lerch (University of Oxford); Leon French (CAMH, University of Toronto)
Learning rich observation models in factor graphs Paloma Sodhi (Carnegie Mellon University)*
Learning the rules: deducing the rules governing animal learning Zoe Ashwood (Princeton University)*; Nicholas A Roy (Princeton Neuroscience Institute); Ji Hyun Bak (UC Berkeley); Jonathan W Pillow (Princeton University)
Machine learning for analyzing tumor burden distribution as a prognostic biomarker for immunotherapy Teresa Bucho (NKI)*; Stefano Trebeschi (The Netherlands Cancer Institute); Zuhir Bodalal Elkarghali (
Methods for Graph Cluster Randomization with Latent Communities Heather Mathews (Duke University)*; Alexander Volfovsky (Duke University)
How Does Varying Depth and Width Affect Neural Network Representations? Thao Nguyen (Google)*; Maithra Raghu (Google); Simon Kornblith (Google Brain)
Understanding the Amazon Forest Using Satellite Imagery Oluwabukola G Adegboro (African Masters in Machine Intelligence (AMMI), AIMS Rwanda)*; Abhishek Das (Facebook AI)
Rebounding Bandits for Modeling Satiation Effects Liu Leqi (Carnegie Mellon University)*; Fatma Kilinc-Karzan (Carnegie Mellon University); Alan Montgomery (Carnegie Mellon University); Zachary Lipton (Carnegie Mellon University)
Using Large-Scale Language Models to Understand Psycho-Linguistic Dimensions of Phishing Kayla Duskin (PNNL)*; Svitlana Volkova (Pacific Northwest National Laboratory)
Towards Autonomous Procurement: Supplier Recommendation in Enterprise Shruti Bhargava (SAP Ariba Machine Learning Business Network)*; Ran Zhou (SAP Ariba Machine Learning Business Network); Tanya Piplani (SAP Ariba Machine Learning Business Network); Varsha Sankar (SAP Ariba Machine Learning Business Network)
T-RECS: A General Simulation Tool to Study the Impact of Recommendation Systems Elena Lucherini (Princeton University)*; Matthew Sun (Princeton University); Arvind Narayanan (Princeton University)
Multi-Task Text Classification using Graph Convolutional Neural Networks for Resource-Poor Language Mounika Marreddy (IIIT Hyderabad)*; Vakada Lakshmi Sireesha (IIIT-Hyderabad); Charan Chinni (IIIT-Hyderabad); Subba Reddy Oota (IIIT Hyderabad); Radhika Mamidi (IIIT Hyderabad)
Improving Neural Storytelling with Commonsense Inferences Xiangyu Peng (Georgia Institute of Technology)*; Siyan Li (Georgia Institute of Technology); Sarah Wiegreffe (Georgia Institute of Technology)
Noisy Neural Network Compression Berivan Isik (Stanford University)*; Kristy Choi (Stanford University); Xin Zheng (Stanford University); Armin Alaghi (University of Washington); Tsachy Weissman (Stanford University); Stefano Ermon (Stanford University); H.-S. Philip Wong (Stanford University)
Multiscale PHATE Exploration of Covid-19 Data Jessie Huang (Yale University)*; MANIK KUCHROO (Yale University); Jean-Christophe Grenier (Université de Montréal); Julie Hussin (Université de Montréal); Guy Wolf (Université de Montréal); Smita Krishnaswamy (Yale University)
Causal model for cognitive load estimation in mixed-reality environments Kinjal Shah (Johns Hopkins University)*; Wenhao Gu (Johns Hopkins University); Mathias Unberath (Johns Hopkins University)
Uncertainty Estimation Methods in the Presence of Noisy Labels Li Chen (Facebook)*; Purvi Goel (Facebook); Ilknur Kaynar Kabul (Facebook)
Accelerating 3D Deep Learning with PyTorch3D Nikhila Ravi (Facebook AI Research)*; Jeremy Reizenstein (Facebook AI Research); David Novotny (Facebook AI Research); Taylor Gordon (Facebook); Wan-Yen Lo (Facebook AI Research); Justin Johnson (Facebook); Georgia Gkioxari (Facebook AI Research)
Estimating Influential Samples in the Fragile Families Challenge Aishwarya Mandyam (Princeton University)*; Siena Dumas Ang (Princeton University); Barbara Engelhardt (Princeton University)
Double-Prong Occupancy ConvLSTM: Spatiotemporal Prediction in Urban Environments Maneekwan Toyungyernsub (Stanford)*; Masha Itkina (Stanford University); Ransalu Senanayake (Stanford University); Mykel Kochenderfer (Stanford University)
Boulder and crater recognition for statistical analysis of collisions on planetary bodies Jennifer Pouplin (Purdue University)*; Jacob Elliott (Purdue University); Alison Pouplin (Technical University of Denmark)
Parametric bootstrap for correcting clamping and truncation bias in differential privacy Cecilia Ferrando (University of Massachusetts, Amherst)*; Daniel Sheldon (University of Massachusetts, Amherst)
From Data-to-Decisions: Learning Representations for End-to-end Sepsis Detection Shinjini Ghosh (Massachusetts Institute of Technology)*; Alexander Amini (Massachusetts Institute of Technology); Daniela Rus (MIT CSAIL)
Variational Occlusion Inference Using People as Sensors Masha Itkina (Stanford University)*; Ye-Ji Mun (University of Illinois at Urbana-Champaign); Katherine Driggs-Campbell (University of Illinois at Urbana-Champaign); Mykel Kochenderfer (Stanford University)
Identification, Classification and Modelling of Traditional AFrican Dances. Adebunmi E Odefunso (Purdue University, West Lafayette)*
Using A Taxonomically-Informed Convolutional Neural Networks Species Distribution Model To Predict Plant Biodiversity Across California From High-Resolution Satellite Imagery Data Lauren Gillespie (Stanford University)*; Moises Exposito-Alonso (Carnegie Science)
Adversarial Evaluation of Binary Deception Classification Ellyn M Ayton (Pacific Northwest National Laboratory)*; Maria Glenski (Pacific Northwest National Laboratory)
Preference-Based Bayesian Optimization in High Dimensions with Human Feedback Myra Cheng (Caltech)*; Ellen Novoseller (Caltech); Maegan Tucker (Caltech); Richard Cheng (Caltech); Joel Burdick (Caltech); Yisong Yue (Caltech)
Learning to navigate in unseen cluttered structured environments Vidhi Jain (Carnegie Mellon University)*; Ganesh Iyer (IIIT Hyderabad); Katia Sycara (Carnegie Mellon University)
RKT : Relation-Aware Self-Attention for Knowledge Tracing Shalini Pandey (University of Minnesota)*; Jaideep Srivastava (University of Minnesota)
Hybrid Forecasting of Service Request Tickets with Human-in-the-loop (HitL) Shubhi Asthana (IBM Research – Almaden)*; Bing Zhang (PhD); Aly Megahed (IBM Research – Almaden); Pawan Chowdhary (IBM Research – Almaden); Taiga Nakamura (IBM Research – Almaden)
Max-value Entropy Search for Multi-Objective Bayesian Optimization with Constraints Syrine Belakaria (Washington State university)*; Aryan Deshwal (Washington state university); Janardhan Rao Doppa (Washington State University)
Evidential Deep Learning for Uncertainty Guided Molecular and Atomistic Property Prediction Ava Soleimany (MIT)*; Alexander Amini (Massachusetts Institute of Technology); Samuel L Goldman (MIT); Daniela Rus (MIT CSAIL); Sangeeta Bhatia (MIT); Connor Coley (MIT)
Fair ML Under Distribution Shift Jessica Dai (Brown University)*; Sarah M Brown (Brown University)
Safe Crowd Navigation in the Presence of Occlusions Ye-Ji Mun (University of Illinois at Urbana-Champaign)*; Masha Itkina (Stanford University); Katherine Driggs-Campbell (University of Illinois at Urbana-Champaign)
Calibration Driven Learning for Building Reliable and Generalizable Deep Predictive Models Bindya Venkatesh (Arizona State University)*; Jayaraman J. Thiagarajan (Lawrence Livermore National Laboratory); Deepta Rajan (IBM Research)
Adaptive Video Subsampling Sameeksha Katoch (Arizona State University)*
Robot Autonomous Motion (RoAM) Dataset for Dynamic Scene Prediction with Camera Motion Meenakshi Sarkar (Indian Institute of Science)*; Vaishnavi Agrawal (VIT University); Prabhu Pradhan (Max Planck Institute for Intelligent Systems); Debasish Ghose (IISc)
Interventional Neural GRU-ODEs Helen Zhou (Carnegie Mellon University)*; Andrew M Dai (Google Brain); Yuan Xue (Google)
Measuring Public Opinion Online: Potentials and Pitfalls Indira Sen (GESIS)*; Fabian Flöck (GESIS Cologne); Katrin Weller (GESIS – Leibniz Institute for the Social Sciences); Claudia Wagner (GESIS-Leibniz Institute for the Social Sciences)
Mining the Stars: Learning Quality Ratings with User-facing Explanations for Vacation Rentals Anastasiia Kornilova (Booking.com)*; Lucas Bernardi (Booking.com)
Impacts of Differentially Private Synthetic Data on Downstream Classification Fairness Jin Cheng (University of Toronto)*; Vinith M Suriyakumar (University of Toronto, Vector Institute); Natalie Dullerud (University of Toronto, Vector Institute); Shalmali Joshi (Harvard University (SEAS)); Marzyeh Ghassemi (University of Toronto, Vector Institute)
SUGAR: A surprise-gated recurrent neural network model for event compression Dania Humaidan (University of Tübingen)*; Martin V. Butz (University of Tübingen)
Calling Out Bluff: Attacking the Robustness of Automatic Scoring Systems with Adversarial Testing Mehar Bhatia (MIDAS Lab, IIIT-Delhi)*; Yaman Kumar (Indraprastha Institute of Information Technology, Delhi); Rajiv Ratn Shah (IIIT Delhi)
Deep Learning with Multi-Dimensional Time-Series Data: Examining the Effect of Music on Movement Benedikte Wallace (University of Oslo)*; Kristian Nymoen (University of Oslo)
A Deep Generative Model for Fair Decision Learning under Partially Labelled Data Miriam Rateike (Max-Planck-Insitute of Intelligent Systems, Tübingen)*; Olga Mineeva (ETH Zurich); Isabel Valera (Uni Saarland)
Classification, Regression or Ordinal Regression? Assessing oral proficiency of non-native English speakers and interpreting results. Pakhi Bamdev (Indraprastha Institute of Information Technology, Delhi)*; Manraj Singh Grover (Indraprastha Institute of Information Technology, Delhi); Yaman Kumar (Indraprastha Institute of Information Technology, Delhi); Payman Vafayee (Columbia University); Mika Hama (SLTI); Rajiv Ratn Shah (IIIT Delhi)
Possibilistic Classification Using Kernel Mahalanobis Distance with Self-Adaptive Parameters Leila Kalantari (University of Florida)*; Jose Principe (University of Florida); Kathryn Sieving (University of Florida)
Correlational Neural Networks: Establishing neural pathways Gagana B (AI Without Borders)*
Modelling pandemic transmission dynamics for decision making and policy response Luz Stefani Sotomayor Valenzuela (Queensland University of Technology)*
Learning Infection Influence Using Self-Excitatory Temporal Point Processes Agni Kumar (Massachusetts Institute of Technology (MIT))*
Sample Complexity of Uniform Convergence for Multicalibration Eliran Shabat (Tel-Aviv University); Lee Cohen (Tel Aviv University)*; Yishay Mansour (Tel Aviv University and Google Research)
Deep Generative Edge-sequence Modeling for Dynamic Graphs Elahe Ghalebi (Vector Institute)*
Reprogramming Language Models for Molecular Representation Learning Ria Vinod (UC Berkeley)*; Pin-Yu Chen (IBM Research); Payel Das (IBM Research)

Copyright © Women in Machine Learning 2020