LES CHERCHEURS DE MILA PRÉSENTERONT LEURS TRAVAUX À L'OCCASION DE LA CONFÉRENCE ICLR 2021

Mila est fier de partager les efforts et les réalisations de ses chercheurs, qui présenteront leurs travaux à l’International Conference on Learning Representations(ICLR). L'événement se déroulera en ligne du 4 au 8 mai.

Les publications acceptées des chercheurs de Mila feront l’objet de présentations orales, d’affiches et même de « spotlights ». Leurs travaux, ainsi que leurs ateliers, couvrent un large éventail de sujets, y compris, mais sans s’y limiter, de nouvelles représentations autoprédictives, des algorithmes d'optimisation des politiques à partir de données fixes, et une approche novatrice pour la prévision de l'infectiosité par une méthode PCT (Proactive Contact Tracing).

La conférence ICLR est reconnue mondialement pour la présentation de recherches de pointe dans le domaine de l'apprentissage automatique, de l’IA et d’autres domaines connexes. C’est l’une des conférences les mieux classées sur la base de son h5-index et son score d’impact (voir : Google Scholar and Guide2Research). Parmi les 2 997 soumissions de cette année, 860 publications ont été acceptées (28,7 %). De ce chiffre, 31 ont été co-rédigées par des membres de Mila.

Voici la liste complète des publications acceptées, ainsi qu’un aperçu des ateliers co-organisés par nos chercheurs :

Publications « spotlight » :

Parameterization of Hypercomplex Multiplications

Aston Zhang, Yi Tay, Shuai Zhang, Anh Tuan Luu, Siu Hui, Jie Fu

Data-Efficient Reinforcement Learning with Self-Predictive Representations

Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron Courville, Philip Bachman

https://arxiv.org/abs/2007.05929

Predicting Infectiousness for Proactive Contact Tracing

Yoshua Bengio, Prateek Gupta, Tegan Maharaj, Nasim Rahaman, Martin Weiss, Tristan Deleu, Eilif Muller, Meng Qu, Victor Schmidt, Pierre-Luc St-Charles, Hannah Alsdurf, Olexa Bilanuik, David Buckeridge, Gáetan Marceau Caron, Pierre-Luc Carrier, Joumana Ghosn, Satya Ortiz-Gagne, Chris Pal, Irina Rish, Bernhard Schölkopf, Abhinav Sharma, Jian Tang, Andrew Williams

https://arxiv.org/abs/2010.12536

Recurrent Independent Mechanisms

Anirudh Goyal, Alex Lamb, Jordan Hoffman, Shagun Sodhani, Yoshua Bengio, Bernhard Schölkopf

https://arxiv.org/abs/1909.10893v6

Regularized Inverse Reinforcement Learning

Wonseok Jeon, Chen-Yang Su, Paule Barde, Thang Doan, Derek Nowrouzezahrai, Joelle Pineau

https://arxiv.org/abs/2010.03691

Systematic generalization with group invariant predictions

Faruk Ahmed, Yoshua Bengio, Harm van Seijen, Aaron Courville

Neural Approximate Sufficient Statistics for Implicit Models

Yanzhi Chen, Dinghuai Zhang, Michael U. Gutmann, Aaron Courville, Zhanxing Zhu

https://arxiv.org/abs/2010.10079

Contrastive Similarity Embeddings for Generalization in Reinforcement Learning

Rishabh Agarwal, Marlos Machado, Pablo Samuel Castro, Marc Bellemare

https://agarwl.github.io/pse/

Présentation orale :

Iterated Learning for Emergent Systematicity in VQA

Ankit Vani, Max Schwarzer, Yuchen Lu, Eeshan Dhekane, Aaron Courville

Présentations par affiches :

CoCon: A Self-Supervised Approach for Controlled Text Generation

Alvin Chan, Yew-Soon Ong, Bill Pung, Aston Zhang, Jie Fu

https://arxiv.org/abs/2006.03535

Adversarial score matching and improved sampling for image generation

Alexia Jolicoeur-Martineau, Rémi Piché-Taillefer, Rémi Tachet des Combes, Ioannis Mitliagkas

https://arxiv.org/abs/2009.05475

The Importance of Pessimism in Fixed-Dataset Policy Optimization

Jacob Buckman, Carles Gelada, Marc G. Bellemare

https://arxiv.org/abs/2009.06799

A Universal Representation Transformer Layer for Few-Shot Image Classification

Lu Liu, William Hamilton, Guodong Long, Jing Jiang, Hugo Larochelle

https://arxiv.org/abs/2006.11702

Saliency is a Possible Red Herring When Diagnosing Poor Generalization

Joseph Viviano, Becks Simpson, Francis Dutil, Yoshua Bengio, Joseph Paul Cohen

https://arxiv.org/abs/1910.00199v2

Integrating Categorical Semantics into Unsupervised Domain Translation

Samuel Lavoie-Marchildon, Faruk Ahmed, Aaron Courville

https://arxiv.org/abs/2010.01262v1

CausalWorld: A Robotic Manipulation Benchmark for Causal Structure and Transfer Learning

Ossama Ahmed, Frederik Träuble, Anirudh Goyal, Alexander Neitz, Manuel Wuthrich, Yoshua Bengio, Bernard Schölkopf, Stefan Bauer

https://arxiv.org/abs/2010.04296

Learning Neural Generative Dynamics for Molecular Conformation Generation

Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, Jian Tang

Meta Attention Networks: Meta-Learning Attention to Modulate Information Between Recurrent Independent Mechanisms

Kanika Madan, Nan Rosemary Ke, Anirudh Goyal, Bernard Schölkopf, Yoshua Bengio

Learning Robust State Abstractions for Hidden-Parameter Block MDPs

Amy Zhang, Shagun Sodhani, Khimya Khetarpal, Joelle Pineau

DC3: A learning method for optimization with hard constraints

Priya L. Donti, David Rolnick, J. Zico Kolter

Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data

Jonathan Pilault, Amine El hattami, Christopher Pal

https://arxiv.org/abs/2009.09139

Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Systems

Anirudh Goyal, Alex Lamb, Phanideep Gampa, Philippe Beaudoin, Charles Blundell, Sergey Levine, Yoshua Bengio, Michael Curtis Mozer

Learning to live with Dale’s principle: ANNs with separate excitatory and inhibitory units

Jonathan Cornford, Damjan Kalajdzievski, Marco Leite, Amélie Lamarquelle, Dimitri Michael Kullmann, Blake Richards

https://www.biorxiv.org/content/10.1101/2020.11.02.364968v1

Reinforcement Learning with Random Delays

Yann Bouteiller, Simon Ramstedt, Giovanni Beltrame, Christopher Pal, Jonathan Binas

https://arxiv.org/abs/2010.02966

gradSim: Differentiable simulation for system identification and visuomotor control

Krishna Murthy, Miles Macklin, Florian Golemo, Vikram Voleti, Linda Petrini, Martin Weiss, Breandan Considine, Jérôme Parent-Lévesque, Kevin Xie, Kenny Erleben, Liam Paull, Florian Shkurti, Derek Nowrouzezahrai, Sanja Fidler

RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs

Meng Qu, Junkun Chen, Louis-Pascal Xhonneux, Yoshua Bengio, Jian Tang

https://arxiv.org/abs/2010.04029

Neural representation and generation for RNA secondary structures

Zichao Yan, William Hamilton, Mathieu Blanchette

https://www.biorxiv.org/content/10.1101/2020.02.11.931030v1

Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization

Chin-Wei Huang, Ricky T. Q. Chen, Christos Tsirigotis, Aaron Courville

https://arxiv.org/abs/2012.05942

Repurposing Pretrained Models for Robust Out-of-domain Few-Shot Learning

Namyeong Kwon, Hwidong Na, Gabriel Huang, Simon Lacoste-Julien

Spatially Structured Recurrent Modules

Nasim Rahaman, Anirudh Goyal, Muhammad Waleed Gondal, Manuel Wuthrich, Stefan Bauer, Yash Sharma, Yoshua Bengio, Bernhard Schölkopf

https://arxiv.org/abs/2007.06533

Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning

Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine

https://arxiv.org/abs/2010.14498

Ateliers :

Geometric and Topological Representation Learning

Guy Wolf, Xiuyuan Cheng, Smita Krishnaswamy, Jure Leskovec, Bastian A. Rieck, Soledad Villar

https://gt-rl.github.io/

A Roadmap to Never-Ending RL

Feryal Behbahani, Khimya Khetarpal, Louis Kirsch, Rose Wang, Annie Xie, Adam White, Doina Precup

Beyond Static Papers: Rethinking How we Share Scientific Understanding in ML

Krishna Murthy Jatavallabhula, Bhairav Mehta, Tegan Maharaj, Amy Tabb, Khimya Khetarpal, Aditya Kusupati, Anna Rogers, Sara Hooker, Breandan Considine, Devi Parikh, Derek Nowrouzezahrai, Yoshua Bengio

https://rethinkingmlpapers.github.io/

Self-Supervision for Reinforcement Learning

Ankesh Anand, Bogdan Mazoure, Amy Zhang, Thang Doan, Khurram Javed, Devon Hjelm, Martha White

Hardware-Aware Efficient Training of Deep Learning Models

Ghouthi Boukli Hacene, Vincent Gripon, François Leduc-Primeau, Vahid Partovi Nia, Andreas Moshovos, Fan Yang, Yoshua Bengio

https://haet2021.github.io/