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

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

Ximeng Mao

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - McGill

Co-superviseur⋅e :

Doctorat - McGill

Hani Zaki

Stagiaire de recherche - UdeM

Co-superviseur⋅e :

Marc-André Legault

Github

Publications

Training End-to-End Dialogue Systems with the Ubuntu Dialogue Corpus

Ryan Thomas Lowe

Nissan Pow

Iulian V. Serban

Laurent Charlin

Chia-Wei Liu

Joelle Pineau

In this paper, we construct and train end-to-end neural network-based dialogue systems using an updated version of the recent Ubuntu Dialogu… (voir plus)e Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This dataset is interesting because of its size, long context lengths, and technical nature; thus, it can be used to train large models directly from data with minimal feature engineering, which can be both time consuming and expensive. We provide baselines in two different environments: one where models are trained to maximize the log-likelihood of a generated utterance conditioned on the context of the conversation, and one where models are trained to select the correct next response from a list of candidate responses. These are both evaluated on a recall task that we call Next Utterance Classification (NUC), as well as other generation-specific metrics. Finally, we provide a qualitative error analysis to help determine the most promising directions for future research on the Ubuntu Dialogue Corpus, and for end-to-end dialogue systems in general.

2017-01-20

Dialogue & Discourse (publié)

doi.org

An Actor-Critic Algorithm for Sequence Prediction

Dzmitry Bahdanau

Philemon Brakel

Kelvin Xu

Anirudh Goyal

Ryan Lowe

Joelle Pineau

Aaron Courville

Yoshua Bengio

We present an approach to training neural networks to generate sequences using actor-critic methods from reinforcement learning (RL). Curren… (voir plus)t log-likelihood training methods are limited by the discrepancy between their training and testing modes, as models must generate tokens conditioned on their previous guesses rather than the ground-truth tokens. We address this problem by introducing a textit{critic} network that is trained to predict the value of an output token, given the policy of an textit{actor} network. This results in a training procedure that is much closer to the test phase, and allows us to directly optimize for a task-specific score such as BLEU. Crucially, since we leverage these techniques in the supervised learning setting rather than the traditional RL setting, we condition the critic network on the ground-truth output. We show that our method leads to improved performance on both a synthetic task, and for German-English machine translation. Our analysis paves the way for such methods to be applied in natural language generation tasks, such as machine translation, caption generation, and dialogue modelling.

2017-01-01

ICLR.cc/2017/conference (poster)

openreview.net

Multitask Spectral Learning of Weighted Automata

Guillaume Rabusseau

Borja Balle

Joelle Pineau

We consider the problem of estimating multiple related functions computed by weighted automata~(WFA). We first present a natural notion of r… (voir plus)elatedness between WFAs by considering to which extent several WFAs can share a common underlying representation. We then introduce the model of vector-valued WFA which conveniently helps us formalize this notion of relatedness. Finally, we propose a spectral learning algorithm for vector-valued WFAs to tackle the multitask learning problem. By jointly learning multiple tasks in the form of a vector-valued WFA, our algorithm enforces the discovery of a representation space shared between tasks. The benefits of the proposed multitask approach are theoretically motivated and showcased through experiments on both synthetic and real world datasets.

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.

2017-01-01

Conference on Empirical Methods in Natural Language Processing (publié)

doi.org

arxiv.org

Recent Advances in Reinforcement Learning

Joelle Pineau

2008-01-01

Lecture Notes in Computer Science (publié)

doi.org

Recent Advances in Reinforcement Learning

Joelle Pineau

2008-01-01