13 Oct 2020

Mila Researchers prep for NeurIPS 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
      Giancarlo Kerg, Bhargav Kanuparthi, Anirudh Goyal, Kyle Goyette, Yoshua BengioGuillaume Lajoie
      https://arxiv.org/abs/2006.09471
    2. Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples
      Samarth Sinha, Zhengli Zhao, Anirudh Goyal, Colin Raffel, Augustus Odena
      https://arxiv.org/abs/2002.06224
    3. Hybrid Models for Learning to Branch
      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
      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
      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
      Aishwarya Sivaraman, Golnoosh Farnadi, Todd Millstein, Guy Van den Broeck
      https://arxiv.org/abs/2006.08852
    7. Adversarial Example Games
      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
      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
      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
      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
      Dinghuai Zhang, Mao Ye, Chengyue Gong, Zhanxing Zhu, Qiang Liu
      https://arxiv.org/abs/2002.09169
    12. Forethought and Hindsight in Credit Assignment
      Veronica Chelu, Doina Precup, Hado van Hasselt
    13. Real World Games Look like Spinning Tops
      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
      Marine Le Morvan, Julie Josse, Thomas Moreau, Erwan Scornet, Gaël Varoquaux
      https://arxiv.org/abs/2007.01627
    15. Training Linear Finite-State Machines
      Arash Ardakani, Amir Ardakani, Warren Gross
    16. RL Unplugged: Benchmarks for Offline Reinforcement Learning
      Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gomez Colmenarejo, Konrad ZolnaRishabh 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
      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
      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
      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
      Yimeng Min, Frederik Wenkel, Guy Wolf
      https://arxiv.org/abs/2003.08414
    21. In search of robust measures of generalization
      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
      Scott Fujimoto, David Meger, Doina Precup
    23. Learning Graph Structure with A Finite-State Automaton Layer
      Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow
      https://arxiv.org/abs/2007.04929
    24. Unsupervised Learning of Dense Visual Representations
      Pedro O. Pinheiro, Amjad Almahairi, Ryan Benmalek, Florian Golemo, Aaron Courville
    25. Equivariant Networks for Hierarchical Structures
      Renhao Wang, Marjan Albooyeh, Siamak Ravanbakhsh
    26. Symbols: Probing Learning Algorithms with Synthetic Datasets
      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
      Shengchao Liu, Dimitris Papailiopoulos, Dimitris Achlioptas
    28. Novelty Search in representational space for sample efficient exploration
      Ruo Yu Tao, Vincent François-Lavet, Joelle Pineau
      https://arxiv.org/abs/2009.13579
    29. La-MAML: Look-ahead Meta Learning for Continual Learning
      Gunshi Gupta, Karmesh Yadav, Liam Paull
      https://arxiv.org/abs/2007.13904
    30. Adversarial Soft Advantage Fitting: Imitation Learning Without Policy Optimization
      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
      Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin
      https://arxiv.org/abs/2007.01754
    32. Reward Propagation using Graph Convolutional Networks
      Martin Klissarov and Doina Precup
      https://arxiv.org/abs/2010.02474
    33. Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence
      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
      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
      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)
      Joey Bose, William Hamilton
      https://sites.google.com/view/diffgeo4dl/
    4. Differentiable vision, graphics, and physics applied to machine learning
      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
      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
      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
      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
      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
      Golnoosh Farnadi, Awa Dieng, Jessica Schrouff, Matt Kusner, Fernando Diaz
      https://www.afciworkshop.org/
    10. AI for Earth Sciences
      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
      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
      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
      Rishabh Agarwal, Aviral Kumar, George Tucker, Doina Precup, Lihong Li
      https://offline-rl-neurips.github.io/
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