Portrait of Joelle Pineau

Joelle Pineau

Core Academic Member
jpineau@mila.quebec
Canada CIFAR AI Chair
Associate Professor, McGill University, School of Computer Science
Co-Manager Director, Meta AI (FAIR - Facebook AI Research)
Research Topics
Medical Machine Learning
Natural Language Processing
Reinforcement Learning
Website
Google Scholar
LinkedIn
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Biography

Joelle Pineau is a professor and William Dawson Scholar at the School of Computer Science, McGill University, where she co-directs the Reasoning and Learning Lab. She is a core academic member of Mila – Quebec Artificial Intelligence Institute, a Canada CIFAR AI Chair, and Chief AI Officer at Cohere. Pineau holds a BSc in systems design engineering from the University of Waterloo, and an MSc and PhD in robotics from Carnegie Mellon University.

Her research focuses on developing new models and algorithms for planning and learning in complex partially observable domains. She also works on applying these algorithms to complex problems in robotics, health care, games and conversational agents. In addition to being on the editorial board of the Journal of Machine Learning Research and past president of the International Machine Learning Society, Pineau is the recipient of numerous awards and honours: NSERC’s E.W.R. Steacie Memorial Fellowship (2018), Governor General Innovation Award (2019), Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), Senior Fellow of the Canadian Institute for Advanced Research (CIFAR), and Fellow of the Royal Society of Canada.

Current Students

Arnaud Bergeron
Master's Research - Université de Montréal
Principal supervisor :
Nicolas Le Roux
arnaud.bergeron1@mila.quebec
Ximeng Mao
PhD - Université de Montréal
Principal supervisor :
Guillaume Lajoie
ximeng.mao@mila.quebec
Website
Github
Google Scholar
Maxime Wabartha
Collaborating Alumni - McGill University
wabarthm@mila.quebec

Publications

Multitask Spectral Learning of Weighted Automata.
Guillaume Rabusseau
Borja Balle
Joelle Pineau
2016-12-31
Neural Information Processing Systems (unknown)
dblp.uni-trier.de
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
Iulian V. Serban
Alessandro Sordoni
Yoshua Bengio
Aaron Courville
Joelle Pineau
We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Ge… (see more)nerative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and back-off n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger question-answer pair corpus and from pretrained word embeddings.
2016-03-04
Proceedings of the AAAI Conference on Artificial Intelligence (published)
doi.org
arxiv.org
Assessing Generalization in TD methods for Deep Reinforcement Learning
Emmanuel Bengio
Doina Precup
Joelle Pineau
2010-12-31
International Conference on Computational Intelligence, Modelling and Simulation (published)
openreview.net
openreview.net
Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces
Bogdan Mazoure
Thang Doan
Tianyu Li
Vladimir Makarenkov
Joelle Pineau
Doina Precup
Guillaume Rabusseau
We propose a general framework for policy representation for reinforcement learning tasks. This framework involves finding a low-dimensional… (see more) embedding of the policy on a reproducing kernel Hilbert space (RKHS). The usage of RKHS based methods allows us to derive strong theoretical guarantees on the expected return of the reconstructed policy. Such guarantees are typically lacking in black-box models, but are very desirable in tasks requiring stability. We conduct several experiments on classic RL domains. The results confirm that the policies can be robustly embedded in a low-dimensional space while the embedded policy incurs almost no decrease in return.
2010-12-31
International Conference on Computational Intelligence, Modelling and Simulation (published)
doi.org
arxiv.org
Recent Advances in Reinforcement Learning
Joelle Pineau
2007-12-31
Lecture Notes in Computer Science (published)
doi.org