167 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Université de MontréalCA31 papers
- McGill UniversityCA16 papers
- Mila - Quebec Artificial Intelligence InstituteCA11 papers
- Concordia UniversityCA5 papers
- Flatiron Health (United States)US5 papers
- Flatiron Institute5 papers
- Google DeepMind (United Kingdom)GB5 papers
- Meta (Israel)IL5 papers
- Canadian Institute for Advanced ResearchCA4 papers
- Polytechnique MontréalCA4 papers
- University of TorontoCA4 papers
- Google (United States)US3 papers
9 papers · 2 filters
Locally Persistent Exploration in Continuous Control Tasks with Sparse Rewards
Susan Amin, Maziar Gomrokchi, Hossein Aboutalebi +2
A major challenge in reinforcement learning is the design of exploration strategies, especially for environments with sparse reward structures and continuous state and action space…
Regularized Inverse Reinforcement Learning
Wonseok Jeon, Chen-Yang Su, Paul Barde +3
Inverse Reinforcement Learning (IRL) aims to facilitate a learner's ability to imitate expert behavior by acquiring reward functions that explain the expert's decisions. Regularize…
Directional Graph Networks
Dominique Beaini, Saro Passaro, Vincent Létourneau +3
The lack of anisotropic kernels in graph neural networks (GNNs) strongly limits their expressiveness, contributing to well-known issues such as over-smoothing. To overcome this lim…
Beyond variance reduction: Understanding the true impact of baselines on policy optimization
Wesley Chung, Valentin Thomas, Marlos C. Machado +1
Bandit and reinforcement learning (RL) problems can often be framed as optimization problems where the goal is to maximize average performance while having access only to stochasti…
Revisiting Fundamentals of Experience Replay
William Fedus, Prajit Ramachandran, Rishabh Agarwal +4
Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but there remain significant gaps in our understanding. We therefore present a systematic…
Stochastic Hamiltonian Gradient Methods for Smooth Games
Nicolas Loizou, Hugo Berard, Alexia Jolicoeur-Martineau +3
The success of adversarial formulations in machine learning has brought renewed motivation for smooth games. In this work, we focus on the class of stochastic Hamiltonian methods a…
What can I do here? A Theory of Affordances in Reinforcement Learning
Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici +2
Reinforcement learning algorithms usually assume that all actions are always available to an agent. However, both people and animals understand the general link between the feature…
Revisiting Loss Modelling for Unstructured Pruning
César Laurent, Camille Ballas, Thomas George +2
By removing parameters from deep neural networks, unstructured pruning methods aim at cutting down memory footprint and computational cost, while maintaining prediction accuracy. I…
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
Xiang Jiang, Qicheng Lao, Stan Matwin +1
We present an approach for unsupervised domain adaptation---with a strong focus on practical considerations of within-domain class imbalance and between-domain class distribution s…