Joelle Pineau

Domain Adversarial Reinforcement Learning

Bonnie Li, Vincent François-Lavet, Thang Doan, Joelle Pineau

Learning Robust State Abstractions for Hidden-Parameter Block MDPs

Amy Zhang, Shagun Sodhani, Khimya Khetarpal, Joelle Pineau

Comments Accepted at the 9th International Conference on Learning Representations. 22 pages, 14 figures

Learning Causal State Representations of Partially Observable Environments

Amy Zhang, Zachary C. Lipton, Luis Pineda, Kamyar Azizzadenesheli, Anima Anandkumar, Laurent Itti, Joelle Pineau, Tommaso Furlanello

Comments 35 pages, 8 figures

Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs

Dora Jambor, Komal Teru, Joelle Pineau, William L. Hamilton

Comments code available at https://github.com/dorajam/few-shot-link-prediction-paper

Journal ref European Chapter of the ACL (EACL), 2021

COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction

Anuroop Sriram, Matthew Muckley, Koustuv Sinha, Farah Shamout, Joelle Pineau, Krzysztof J. Geras, Lea Azour, Yindalon Aphinyanaphongs, Nafissa Yakubova, William Moore

Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)

Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d'Alché-Buc, Emily Fox, Hugo Larochelle

Comments To appear at JMLR, 16 pages + Appendix

Intervention Design for Effective Sim2Real Transfer

Melissa Mozifian, Amy Zhang, Joelle Pineau, David Meger

Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations

Kalesha Bullard, Franziska Meier, Douwe Kiela, Joelle Pineau, Jakob Foerster

Regularized Inverse Reinforcement Learning

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

Comments 26 pages, 7 figures

The importance of transparency and reproducibility in artificial intelligence research

Benjamin Haibe-Kains, George Alexandru Adam, Ahmed Hosny, Farnoosh Khodakarami, MAQC Society Board, Levi Waldron, Bo Wang, Chris McIntosh, Anshul Kundaje, Casey S. Greene, Michael M. Hoffman, Jeffrey T. Leek, Wolfgang Huber, Alvis Brazma, Joelle Pineau, Robert Tibshirani, Trevor Hastie, John P. A. Ioannidis, John Quackenbush, Hugo J. W. L. Aerts

Journal ref Nature 586 (2020) E14-E16

Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces

Bogdan Mazoure, Thang Doan, Tianyu Li, Vladimir Makarenkov, Joelle Pineau, Doina Precup, Guillaume Rabusseau

Constrained Markov Decision Processes via Backward Value Functions

Harsh Satija, Philip Amortila, Joelle Pineau

How To Evaluate Your Dialogue System: Probe Tasks as an Alternative for Token-level Evaluation Metrics

Prasanna Parthasarathi, Joelle Pineau, Sarath Chandar

Online Learned Continual Compression with Adaptive Quantization Modules

Lucas Caccia, Eugene Belilovsky, Massimo Caccia, Joelle Pineau

Improving Sample Efficiency in Model-Free Reinforcement Learning from Images

Denis Yarats, Amy Zhang, Ilya Kostrikov, Brandon Amos, Joelle Pineau, Rob Fergus

Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning

Thang Doan, Bogdan Mazoure, Moloud Abdar, Audrey Durand, Joelle Pineau, R Devon Hjelm

TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning?

Joshua Romoff, Peter Henderson, David Kanaa, Emmanuel Bengio, Ahmed Touati, Pierre-Luc Bacon, Joelle Pineau

Comments Presented at the Theoretical Foundations of Reinforcement Learning workshop at ICML 2020

Deep interpretability for GWAS

Deepak Sharma, Audrey Durand, Marc-André Legault, Louis-Philippe Lemieux Perreault, Audrey Lemaçon, Marie-Pierre Dubé, Joelle Pineau

Comments Accepted at ICML 2020 workshop on ML Interpretability for Scientific Discovery

On the interaction between supervision and self-play in emergent communication

Ryan Lowe, Abhinav Gupta, Jakob Foerster, Douwe Kiela, Joelle Pineau

Comments The first two authors contributed equally. Accepted at ICLR 2020

Stable Policy Optimization via Off-Policy Divergence Regularization

Ahmed Touati, Amy Zhang, Joelle Pineau, Pascal Vincent

Journal ref Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR volume 124, 2020

Invariant Causal Prediction for Block MDPs

Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup

Comments Accepted to ICML 2020. 16 pages, 8 figures

A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM

Iulian Vlad Serban, Varun Gupta, Ekaterina Kochmar, Dung D. Vu, Robert Belfer, Joelle Pineau, Aaron Courville, Laurent Charlin, Yoshua Bengio

Comments 6 pages, 1 figure, 1 table, accepted for publication in the 21st International Conference on Artificial Intelligence in Education (AIED 2020)

Automated Personalized Feedback Improves Learning Gains in an Intelligent Tutoring System

Ekaterina Kochmar, Dung Do Vu, Robert Belfer, Varun Gupta, Iulian Vlad Serban, Joelle Pineau

Comments To be published in Proceedings of the the 21st International Conference on Artificial Intelligence in Education (AIED 2020)

Plan2Vec: Unsupervised Representation Learning by Latent Plans

Ge Yang, Amy Zhang, Ari S. Morcos, Joelle Pineau, Pieter Abbeel, Roberto Calandra

Comments code available at https://geyang.github.io/plan2vec

Journal ref Proceedings of Machine Learning Research, the 2nd Annual Conference on Learning for Dynamics and Control (2020) Volume 120, 1-12

Learning an Unreferenced Metric for Online Dialogue Evaluation

Koustuv Sinha, Prasanna Parthasarathi, Jasmine Wang, Ryan Lowe, William L. Hamilton, Joelle Pineau

Comments Accepted at ACL 2020, 5 pages

Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning

Mahmoud Assran, Joshua Romoff, Nicolas Ballas, Joelle Pineau, Michael Rabbat

Journal ref Advances in Neural Information Processing Systems (2019) 13299-13309

Evaluating Logical Generalization in Graph Neural Networks

Koustuv Sinha, Shagun Sodhani, Joelle Pineau, William L. Hamilton

Interference and Generalization in Temporal Difference Learning

Emmanuel Bengio, Joelle Pineau, Doina Precup

Comments Submitted to ICML 2020. 20 pages, 14 figures

Scalable Multi-Agent Inverse Reinforcement Learning via Actor-Attention-Critic

Wonseok Jeon, Paul Barde, Derek Nowrouzezahrai, Joelle Pineau

TarMAC: Targeted Multi-Agent Communication

Abhishek Das, Théophile Gervet, Joshua Romoff, Dhruv Batra, Devi Parikh, Michael Rabbat, Joelle Pineau

Comments ICML 2019