Mila Researchers prep for NeurIPS 2020

October 13, 2020

Earlier this October, the Neural Information Processing Systems Conference (NeurIPS) unveiled the complete list of accepted publications for its 34th edition, which will be entirely virtual.

As one of the largest and most important machine learning conferences in the world, the research culture around the submission process has become increasingly competitive. To give an example, the NeurIPS 2020 Chairs have announced that of the 12,115 abstracts submitted, a total of 1,903 have been accepted for this year's conference.

In light of the increasing pressure our researchers are facing, it is important to commemorate all the work our students and professors have done in producing their research and organizing workshops for this conference. Accepted papers from our community cover a range of topics including, but not limited to, novel continual model-agnostic meta-learning approaches, solutions to oversmoothness in graph convolutional networks, and two policy regularization methods in multi-agent deep reinforcement learning.

Below is the complete list of publications, as well as an overview of the workshops co-organized by Mila members:

    1. Untangling tradeoffs between recurrence and self-attention in neural networks

      2. Giancarlo Kerg, Bhargav Kanuparthi, Anirudh Goyal, Kyle Goyette, Yoshua Bengio, Guillaume Lajoie

      https://arxiv.org/abs/2006.09471

    2. Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples

      3. Samarth Sinha, Zhengli Zhao, Anirudh Goyal, Colin Raffel, Augustus Odena

      https://arxiv.org/abs/2002.06224

    3. Hybrid Models for Learning to Branch

      4. Prateek Gupta, Maxime Gasse, Elias B. Khalil, M. Pawan Kumar, Andrea Lodi, Yoshua Bengio

      https://arxiv.org/abs/2006.15212

    4. The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning

      5. Harm van Seijen, Hadi Nekoei, Evan Racah, Sarath Chandar

      https://arxiv.org/abs/2007.03158

    5. Your GAN is Secretly an Energy-based Model and You Should use Discriminator Latent Sampling

      6. Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, Yoshua Bengio

      https://arxiv.org/abs/2003.06060

    6. Counterexample-Guided Learning of Monotonic Neural Networks

      7. Aishwarya Sivaraman, Golnoosh Farnadi, Todd Millstein, Guy Van den Broeck

      https://arxiv.org/abs/2006.08852

    7. Adversarial Example Games

      8. Joey Bose, Gauthier Gidel, Hugo Berard, Andre Cianflone, Pascal Vincent, Simon Lacoste-Julien, William Hamilton

      https://arxiv.org/abs/2007.00720

    8. Online Fast Adaptation and Knowledge Accumulation: A New Approach to Continual Learning

      9. Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Caccia, Issam Laradji, Irina Rish, Alexandre Lacoste, David Vazquez, Laurent Charlin

      https://arxiv.org/abs/2003.05856

    9. Learning Dynamic Belief Graphs to Generalize on Text-Based Games

      10. Ashutosh Adhikari, Xingdi Yuan, Marc-Alexandre Côté, Mikulás Zelinka, Marc-Antoine Rondeau, Romain Laroche, Pascal Poupart, Jian Tang, Adam Trischler, William Hamilton

      https://arxiv.org/abs/2002.09127

    10. Measuring Systematic Generalization in Neural Proof Generation with Transformers

      11. Nicolas Gontier, Koustuv Sinha, Siva Reddy, Christopher Pal

      https://arxiv.org/abs/2009.14786

    11. Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework

      12. Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu

      https://arxiv.org/abs/2002.09169

    12. Forethought and Hindsight in Credit Assignment

      13. Veronica Chelu, Doina Precup, Hado van Hasselt

    13. Real World Games Look like Spinning Tops

      14. Wojciech Marian Czarnecki, Gauthier Gidel, Brendan Tracy, Karl Tuyls, Shayegan Omidshafiefi, David Balduzzi, Max Jaderberg

      https://arxiv.org/abs/2004.09468

    14. Neumann Networks: Differential programming for supervised learning with missing values

      15. Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gaël Varoquaux

      https://arxiv.org/abs/2007.01627

    15. Training Linear Finite-State Machines

      16. Arash Ardakani, Amir Ardakani, Warren Gross

    16. RL Unplugged: Benchmarks for Offline Reinforcement Learning

      17. Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gomez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, Nando de Freitas

      https://arxiv.org/abs/2006.13888

    17. Graph Policy Network for Transferable Active Learning on Graphs

      18. Shengding Hu, Zheng Xiong, Xingdi Yuan, Marc-Alexandre Côté, Zhiyuan Liu, Jian Tang

      https://arxiv.org/abs/2006.13463

    18. Towards Interpretable Natural Language Understanding with Explanations as Latent Variables

      19. Wangchunshu Zhou, Jinyi Hu, Hanlin Zhang, Xiaodan Liang, Maosong Sun, Chenyan Xiong, Jian Tang

    19. Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning

      20. Julien Roy, Paul Barde, Félix Harvey, Derek Nowrouzezahrai, Christopher Pal

      https://arxiv.org/abs/1908.02269

    20. Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks

      21. Yimeng Min, Frederik Wenkel, Guy Wolf

      https://arxiv.org/abs/2003.08414

    21. In search of robust measures of generalization

      22. Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel Roy

    22. An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay

      23. Scott Fujimoto, David Meger, Doina Precup

    23. Learning Graph Structure with A Finite-State Automaton Layer

      24. Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow

      https://arxiv.org/abs/2007.04929

    24. Unsupervised Learning of Dense Visual Representations

      25. Pedro O. Pinheiro, Amjad Almahairi, Ryan Benmalek, Florian Golemo, Aaron Courville

    25. Equivariant Networks for Hierarchical Structures

      26. Renhao Wang, Marjan Albooyeh, Siamak Ravanbakhsh

    26. Symbols: Probing Learning Algorithms with Synthetic Datasets

      27. Alexandre Lacoste, Pau Rodríguez, Frederic Branchaud-Charron, Massimo Caccia, Issam Hadj Laradji, Alexandre Drouin, Matthew Craddock, Laurent Charlin, David Vázquez

      https://arxiv.org/abs/2009.06415

    27. Explicit Regularization is Stronger than Implicit Bias: A Study of SGD around Bad Global Minima

      28. Shengchao Liu, Dimitris Papailiopoulos, Dimitris Achlioptas

    28. Novelty Search in representational space for sample efficient exploration

      29. Ruo Yu Tao, Vincent François-Lavet, Joelle Pineau

      https://arxiv.org/abs/2009.13579

    29. La-MAML: Look-ahead Meta Learning for Continual Learning

      30. Gunshi Gupta, Karmesh Yadav, Liam Paull

      https://arxiv.org/abs/2007.13904

    30. Adversarial Soft Advantage Fitting: Imitation Learning Without Policy Optimization

      31. Paul Barde, Julien Roy, Wonseok Jeon, Joelle Pineau, Christopher Pal, Derek Nowrouzezahrai

      https://arxiv.org/abs/2006.13258

    31. Differentiable Causal Discovery from Interventional Data

      32. Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin

      https://arxiv.org/abs/2007.01754

    32. Reward Propagation using Graph Convolutional Networks

      33. Martin Klissarov and Doina Precup

      https://arxiv.org/abs/2010.02474

    33. Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence

      34. Bastian Rieck, Tristan Yates, Christian Bock, Karsten Borgwardt, Guy Wolf, Nicholas Turk-Browne, Smita Krishnaswamy

      https://arxiv.org/abs/2006.07882

Workshops:

    1. Tackling Climate Change with Machine Learning

      2. Tegan Maharaj, Priya Donti, Lynn Kaack, Alexandre Lacoste, Andrew Ng, John Platt, Jennifer Chayes, Yoshua Bengio

      https://www.climatechange.ai/events/neurips2019.html

    2. Topological Data Analysis and Beyond

      3. Bastian Rieck, Frederic Chazal, Smita Krishnaswamy, Roland Kwitt, Karthikeyan Natesan Ramamurthy, Yuhei Umeda, and Guy Wolf

      https://tda-in-ml.github.io/

    3. Differential Geometry meets Deep Learning (DiffGeo4DL)

      4. Joey Bose, William Hamilton

      https://sites.google.com/view/diffgeo4dl/

    4. Differentiable vision, graphics, and physics applied to machine learning

      5. Krishna Murthy, Kelsey Allen, Victoria Dean, Johanna Hansen, Shuran Song, Florian Shkurti, Liam Paull, Derek Nowrouzezahrai, Josh Tenenbaum

      https://montrealrobotics.ca/diffcvgp/

    5. Resistance AI

      6. Mattie Tesfaldet, J Khadijah Abdurahman, William Agnew, Abeba Birhane, Elliot Creager, Agata Foryciarz, Pratyusha Ria Kalluri, Sayash Kapoor, Raphael Gontijo Lopes, Manuel Sabin, Marie-Therese Png, Maria Skoularidou, Ramon Vilarino, Rose E. Wang

      https://sites.google.com/view/resistance-ai-neurips-20/home

    6. Biological and Artificial Reinforcement Learning

      7. Raymond Chua, Feryal Behbahani, Sara Zannone, Doina Precup, Ida Momennejad, Blake Richards, Rui Ponte Costa

      https://sites.google.com/view/biologicalandartificialrl

    7. ML Retrospectives, Surveys & Meta-Analyses

      8. Chhavi Yadav, Prabhu Pradhan, Abhishek Gupta, Peter Henderson, Ryan Lowe, Jessica Forde, Jesse Dodge, Mayoore Jaiswal, Joelle Pineau

      https://ml-retrospectives.github.io/neurips2020/

    8. Object Representations for Learning and Reasoning

      9. William Agnew, Rim Assouel, Michael Chang, Antonia Creswell, Eliza Kosoy, Aravind Rajeswaran, Sjoerd van Steenkiste

      https://orlrworkshop.github.io/index.html

    9. Algorithmic Fairness through the Lens of Causality and Interpretability

      10. Golnoosh Farnadi, Awa Dieng, Jessica Schrouff, Matt Kusner, Fernando Diaz

      https://www.afciworkshop.org/

    10. AI for Earth Sciences

      11. Natasha Dudek, S. Karthik Mukkavilli, Johanna Hansen, Kelly Kochanski, S. Karthik Mukkavilli, Tom Beucler, Karthik Kashinath, Mayur Mudigonda, Amy McGovern

      https://ai4earthscience.github.io/neurips-2020-workshop/organizers.html 

    11. ML4H: Machine Learning for Health

      12. Benjamin Akera, Emily Alsentzer, Oliver J. Bear, Ioana Bica, Irene Chen, Fabian Falck, Stephanie Hyland, Dani Kyasseh, Matthew McDermott, Ehi Nosakhare, Charles Onu, Stephen Pfohl, Aahlad Manas Puli, Suproteem Sarkar, Allen Schmaltz

      https://ml4health.github.io/2020/pages/organizers.html

    12. Women in Machine Learning

      13. Amy Zhang, Elizabeth Wood, Mel Woghiren, Kristy Choi, Raquel Aoki, Judy Hanwen Shen, Belén Saldías, Xenia Miscouridou, Krystal Maughan, Xinyi Chen, Erin Grant

      https://wimlworkshop.org/neurips2020/contact-us/

    13. Offline Reinforcement Learning

      14. Rishabh Agarwal, Aviral Kumar, George Tucker, Doina Precup, Lihong Li

      https://offline-rl-neurips.github.io/