Mila is proud to share the efforts and achievements of its researchers who will be showcasing their work at the International Conference on Learning Representations (ICLR), running online this year from May 4 to 8. 

Accepted papers (spotlight, oral and poster presentations) and workshops from Mila Researchers cover a wide range of topics, including Self-Predictive Representations, fixed-dataset policy optimization algorithms and a novel approach to predicting infectiousness using a DL-based Proactive Contact Tracing (PCT) method. 

ICLR is globally renowned for presenting cutting-edge research in machine learning and AI related fields. It is one of the highest ranked conferences based on its H5-index and Impact Score (See: Google Scholar and Guide2Research). Of the 2,997 submissions this year, 860 papers made it through (28.7%); 31 of those papers were co-authored by Mila researchers.

Papers and workshops co-authored and co-organized by Mila members:

Accepted Papers (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

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

Recurrent Independent Mechanisms

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

Regularized Inverse Reinforcement Learning

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

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

Contrastive Similarity Embeddings for Generalization in Reinforcement Learning

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

Accepted as oral:

Iterated Learning for Emergent Systematicity in VQA

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

Accepted as poster:

CoCon: A Self-Supervised Approach for Controlled Text Generation

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

Adversarial score matching and improved sampling for image generation

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

The Importance of Pessimism in Fixed-Dataset Policy Optimization

Jacob Buckman, Carles Gelada, Marc G. Bellemare

A Universal Representation Transformer Layer for Few-Shot Image Classification

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

Saliency is a Possible Red Herring When Diagnosing Poor Generalization

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

Integrating Categorical Semantics into Unsupervised Domain Translation

Samuel Lavoie-Marchildon, Faruk Ahmed, Aaron Courville

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

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

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

Reinforcement Learning with Random Delays

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

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

Neural representation and generation for RNA secondary structures

Zichao Yan, William Hamilton, Mathieu Blanchette

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

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

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

Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning

Aviral Kumar, Rishabh Agarwal, Dibya Ghosh, Sergey Levine


Geometric and Topological Representation Learning

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

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

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