Portrait de Joelle Pineau

Joelle Pineau

Membre académique principal
jpineau@mila.quebec
Chaire en IA Canada-CIFAR
Professeure agrégée, McGill University, École d'informatique
Co-directrice générale, Meta AI (FAIR - Facebook AI Research)
Sujets de recherche
Apprentissage automatique médical
Apprentissage par renforcement
Traitement du langage naturel
Site web
Google Scholar
LinkedIn
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Biographie

Joelle Pineau est professeure agrégée et titulaire d’une bourse William Dawson à l'Université McGill, où elle codirige le Laboratoire de raisonnement et d'apprentissage. Elle est membre du corps professoral de Mila – Institut québécois d’intelligence artificielle et titulaire d'une chaire en IA Canada-CIFAR. Elle est également vice-présidente de la recherche en IA chez Meta (anciennement Facebook), où elle dirige l'équipe FAIR (Fundamental AI Research). Elle détient un baccalauréat ès sciences en génie de l'Université de Waterloo et une maîtrise et un doctorat en robotique de l'Université Carnegie Mellon.

Ses recherches sont axées sur le développement de nouveaux modèles et algorithmes pour la planification et l'apprentissage dans des domaines complexes partiellement observables. Elle travaille également sur l'application de ces algorithmes à des problèmes complexes en robotique, dans les soins de santé, dans les jeux et dans les agents conversationnels. Elle est membre du comité de rédaction du Journal of Artificial Intelligence Research et du Journal of Machine Learning Research, et est actuellement présidente de l'International Machine Learning Society. Elle a été lauréate de la bourse commémorative E. W. R. Steacie du Conseil de recherches en sciences naturelles et en génie (CRSNG) 2018 et du Prix du Gouverneur général pour l'innovation 2019. Elle est membre de l'Association pour l'avancement de l'intelligence artificielle (AAAI), membre principal de l'Institut canadien de recherches avancées (CIFAR) et membre de la Société royale du Canada.

Étudiants actuels

Arnaud Bergeron
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Nicolas Le Roux
arnaud.bergeron1@mila.quebec
Ximeng Mao
Doctorat - UdeM
Superviseur⋅e principal⋅e :
Guillaume Lajoie
ximeng.mao@mila.quebec
Site web
Github
Google Scholar
Raesetje Sefala
Doctorat - McGill
Co-superviseur⋅e :
Nicolas Le Roux
raesetje.sefala@mila.quebec
Site web
Google Scholar
Maxime Wabartha
Collaborateur·rice alumni - McGill
wabarthm@mila.quebec

Publications

Piecewise Latent Variables for Neural Variational Text Processing
Iulian V. Serban
Alexander G. Ororbia II
Joelle Pineau
Aaron Courville
Advances in neural variational inference have facilitated the learning of powerful directed graphical models with continuous latent variable… (voir plus)s, such as variational autoencoders. The hope is that such models will learn to represent rich, multi-modal latent factors in real-world data, such as natural language text. However, current models often assume simplistic priors on the latent variables - such as the uni-modal Gaussian distribution - which are incapable of representing complex latent factors efficiently. To overcome this restriction, we propose the simple, but highly flexible, piecewise constant distribution. This distribution has the capacity to represent an exponential number of modes of a latent target distribution, while remaining mathematically tractable. Our results demonstrate that incorporating this new latent distribution into different models yields substantial improvements in natural language processing tasks such as document modeling and natural language generation for dialogue.
2016-12-31
Conference on Empirical Methods in Natural Language Processing (publié)
doi.org
arxiv.org
Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses
Ryan Lowe
Michael Noseworthy
Iulian V. Serban
Nicolas A.-Gontier
Yoshua Bengio
Joelle Pineau
Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automa… (voir plus)tic evaluation metrics are biased and correlate very poorly with human judgements of response quality. Yet having an accurate automatic evaluation procedure is crucial for dialogue research, as it allows rapid prototyping and testing of new models with fewer expensive human evaluations. In response to this challenge, we formulate automatic dialogue evaluation as a learning problem. We present an evaluation model (ADEM) that learns to predict human-like scores to input responses, using a new dataset of human response scores. We show that the ADEM model's predictions correlate significantly, and at a level much higher than word-overlap metrics such as BLEU, with human judgements at both the utterance and system-level. We also show that ADEM can generalize to evaluating dialogue models unseen during training, an important step for automatic dialogue evaluation.
2016-12-31
Annual Meeting of the Association for Computational Linguistics (publié)
doi.org
arxiv.org
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… (voir plus)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 (publié)
doi.org
arxiv.org
Recent Advances in Reinforcement Learning
Joelle Pineau
2007-12-31
Lecture Notes in Computer Science (publié)
doi.org