MILA RESEARCHERS TO SHOWCASE THEIR WORK AT ICLR 2021

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
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/

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
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

Workshops:

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/