13 Oct 2020

NeurIPS 2020 : Les chercheurs de Mila se préparent en grand nombre

La conférence sur les systèmes de traitement de l’information neuronale (NeurIPS) a dévoilé la liste complète des publications acceptées en prévision de sa 34e édition qui sera entièrement virtuelle.

Les titulaires des chaires NeurIPS 2020 ont annoncé que sur les 12 115 publications soumises, 1 903 au total ont été acceptées pour la conférence de cette année. Ce nombre très élevé est une illustration que la culture de la recherche devient de plus en plus compétitive.

Au vu de la pression croissante à laquelle nos chercheurs sont confrontés, il est important de commémorer tout le travail que nos étudiants et professeurs ont accompli en produisant leurs recherches et en organisant des ateliers pour la conférence.

Les publications acceptées de notre communauté couvrent un éventail de sujets, y compris, mais sans s’y limiter, de nouvelles approches de méta-apprentissage continu agnostique par modèle, des solutions à l’oversmoothness dans les réseaux convolutionnels de graphes, et deux méthodes de régularisation des politiques dans l’apprentissage de renforcement profond multi-agents.

Voici la liste complète des publications acceptées, ainsi qu’un aperçu des ateliers co-organisés par des membres de Mila (en anglais) :

          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

      Ateliers :

          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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