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 VP of AI research at Meta (previously Facebook), where she leads the Fundamental AI Research (FAIR) team. 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

Ximeng Mao
PhD - Université de Montréal
Principal supervisor :
Guillaume Lajoie
Github
Google Scholar
Raesetje Sefala
PhD - McGill University
Co-supervisor :
Nicolas Le Roux
Website
Google Scholar
Maxime Wabartha
PhD - McGill University

Publications

Advancing science- and evidence-based AI policy.
Rishi Bommasani
Sanjeev Arora
Jennifer Chayes
Yejin Choi
Mariano-Florentino Cuéllar
Li Fei-Fei
Daniel E. Ho
Dan Jurafsky
Sanmi Koyejo
Hima Lakkaraju
Arvind Narayanan
Alondra Nelson
Emma Pierson
Joelle Pineau
Scott R. Singer
Gael Varoquaux
Suresh Venkatasubramanian
Ion Stoica
Percy Liang
Dawn Song
Policy must be informed by, but also facilitate the generation of, scientific evidence.
2025-07-31
ArXiv (preprint)
doi.org
arxiv.org
Advancing science- and evidence-based AI policy.
Rishi Bommasani
Sanjeev Arora
Jennifer Chayes
Yejin Choi
Mariano-Florentino Cuéllar
Li Fei-Fei
Daniel E. Ho
Dan Jurafsky
Sanmi Koyejo
Hima Lakkaraju
Arvind Narayanan
Alondra Nelson
Emma Pierson
Joelle Pineau
Scott Singer
Gael Varoquaux
Suresh Venkatasubramanian
Ion Stoica
Percy Liang
Dawn Song
2025-07-31
Science (published)
doi.org
arxiv.org
Safe Domain Randomization via Uncertainty-Aware Out-of-Distribution Detection and Policy Adaptation
Mohamad H. Danesh
Maxime Wabartha
Stanley Wu
Joelle Pineau
Hsiu-Chin Lin
Deploying reinforcement learning (RL) policies in real-world involves significant challenges, including distribution shifts, safety concerns… (see more), and the impracticality of direct interactions during policy refinement. Existing methods, such as domain randomization (DR) and off-dynamics RL, enhance policy robustness by direct interaction with the target domain, an inherently unsafe practice. We propose Uncertainty-Aware RL (UARL), a novel framework that prioritizes safety during training by addressing Out-Of-Distribution (OOD) detection and policy adaptation without requiring direct interactions in target domain. UARL employs an ensemble of critics to quantify policy uncertainty and incorporates progressive environmental randomization to prepare the policy for diverse real-world conditions. By iteratively refining over high-uncertainty regions of the state space in simulated environments, UARL enhances robust generalization to the target domain without explicitly training on it. We evaluate UARL on MuJoCo benchmarks and a quadrupedal robot, demonstrating its effectiveness in reliable OOD detection, improved performance, and enhanced sample efficiency compared to baselines.
2025-07-08
ArXiv (preprint)
arxiv.org
arxiv.org
A flexible machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition
Marc-André Legault
Jason Hartford
Benoît J. Arsenault
Archer Yang
Joelle Pineau
2025-05-01
American Journal of Human Genetics (published)
doi.org
Rethinking Machine Learning Benchmarks in the Context of Professional Codes of Conduct
Peter Henderson
Jieru Hu
Mona Diab
Joelle Pineau
2024-03-12
Proceedings of the Symposium on Computer Science and Law (published)
doi.org
On the Societal Impact of Open Foundation Models
Sayash Kapoor
Rishi Bommasani
Kevin Klyman
Shayne Longpre
Ashwin Ramaswami
Peter Cihon
Aspen Hopkins
Kevin Bankston
Stella Biderman
Miranda Bogen
Rumman Chowdhury
Alex Engler
Peter Henderson
Yacine Jernite
Seth Lazar
Stefano Maffulli
Alondra Nelson
Joelle Pineau
Aviya Skowron
Dawn Song … (see 5 more)
Victor Storchan
Daniel Zhang
Daniel E. Ho
Percy Liang
Arvind Narayanan
Foundation models are powerful technologies: how they are released publicly directly shapes their societal impact. In this position paper, w… (see more)e focus on open foundation models, defined here as those with broadly available model weights (e.g. Llama 2, Stable Diffusion XL). We identify five distinctive properties (e.g. greater customizability, poor monitoring) of open foundation models that lead to both their benefits and risks. Open foundation models present significant benefits, with some caveats, that span innovation, competition, the distribution of decision-making power, and transparency. To understand their risks of misuse, we design a risk assessment framework for analyzing their marginal risk. Across several misuse vectors (e.g. cyberattacks, bioweapons), we find that current research is insufficient to effectively characterize the marginal risk of open foundation models relative to pre-existing technologies. The framework helps explain why the marginal risk is low in some cases, clarifies disagreements about misuse risks by revealing that past work has focused on different subsets of the framework with different assumptions, and articulates a way forward for more constructive debate. Overall, our work helps support a more grounded assessment of the societal impact of open foundation models by outlining what research is needed to empirically validate their theoretical benefits and risks.
2024-02-27
ArXiv (preprint)
doi.org
arxiv.org
On the Societal Impact of Open Foundation Models
Sayash Kapoor
Rishi Bommasani
Kevin Klyman
Shayne Longpre
Ashwin Ramaswami
Peter Cihon
Aspen Hopkins
Kevin Bankston
Stella Biderman
Miranda Bogen
Rumman Chowdhury
Alex Engler
Peter Henderson
Yacine Jernite
Seth Lazar
Stefano Maffulli
Alondra Nelson
Joelle Pineau
Aviya Skowron
Dawn Song … (see 5 more)
Victor Storchan
Daniel Zhang
Daniel E. Ho
Percy Liang
Arvind Narayanan
Foundation models are powerful technologies: how they are released publicly directly shapes their societal impact. In this position paper, w… (see more)e focus on open foundation models, defined here as those with broadly available model weights (e.g. Llama 2, Stable Diffusion XL). We identify five distinctive properties (e.g. greater customizability, poor monitoring) of open foundation models that lead to both their benefits and risks. Open foundation models present significant benefits, with some caveats, that span innovation, competition, the distribution of decision-making power, and transparency. To understand their risks of misuse, we design a risk assessment framework for analyzing their marginal risk. Across several misuse vectors (e.g. cyberattacks, bioweapons), we find that current research is insufficient to effectively characterize the marginal risk of open foundation models relative to pre-existing technologies. The framework helps explain why the marginal risk is low in some cases, clarifies disagreements about misuse risks by revealing that past work has focused on different subsets of the framework with different assumptions, and articulates a way forward for more constructive debate. Overall, our work helps support a more grounded assessment of the societal impact of open foundation models by outlining what research is needed to empirically validate their theoretical benefits and risks.
2024-02-27
ArXiv (preprint)
doi.org
arxiv.org
On the Societal Impact of Open Foundation Models
Sayash Kapoor
Rishi Bommasani
Kevin Klyman
Shayne Longpre
Ashwin Ramaswami
Peter Cihon
Aspen Hopkins
Kevin Bankston
Stella Biderman
Miranda Bogen
Rumman Chowdhury
Alex Engler
Peter Henderson
Yacine Jernite
Seth Lazar
Stefano Maffulli
Alondra Nelson
Joelle Pineau
Aviya Skowron
Dawn Song … (see 5 more)
Victor Storchan
Daniel Zhang
Daniel E. Ho
Percy Liang
Arvind Narayanan
Foundation models are powerful technologies: how they are released publicly directly shapes their societal impact. In this position paper, w… (see more)e focus on open foundation models, defined here as those with broadly available model weights (e.g. Llama 2, Stable Diffusion XL). We identify five distinctive properties (e.g. greater customizability, poor monitoring) of open foundation models that lead to both their benefits and risks. Open foundation models present significant benefits, with some caveats, that span innovation, competition, the distribution of decision-making power, and transparency. To understand their risks of misuse, we design a risk assessment framework for analyzing their marginal risk. Across several misuse vectors (e.g. cyberattacks, bioweapons), we find that current research is insufficient to effectively characterize the marginal risk of open foundation models relative to pre-existing technologies. The framework helps explain why the marginal risk is low in some cases, clarifies disagreements about misuse risks by revealing that past work has focused on different subsets of the framework with different assumptions, and articulates a way forward for more constructive debate. Overall, our work helps support a more grounded assessment of the societal impact of open foundation models by outlining what research is needed to empirically validate their theoretical benefits and risks.
2024-02-27
ArXiv (preprint)
doi.org
arxiv.org
A novel and efficient machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition
Marc-André Legault
Jason Hartford
Benoît J. Arsenault
Y. Archer
Yang
Joelle Pineau
Mendelian Randomization (MR) enables estimation of causal effects while controlling for unmeasured confounding factors. However, traditional… (see more) MR's reliance on strong parametric assumptions can introduce bias if these are violated. We introduce a new machine learning MR estimator named Quantile Instrumental Variable (IV) that achieves low estimation error in a wide range of plausible MR scenarios. Quantile IV is distinctive in its ability to estimate nonlinear and heterogeneous causal effects and offers a flexible approach for subgroup analysis. Applying Quantile IV, we investigate the impact of circulating sclerostin levels on heel bone mineral density, osteoporosis, and cardiovascular outcomes in the UK Biobank. Employing various MR estimators and colocalization techniques that allow multiple causal variants, our analysis reveals that a genetically predicted reduction in sclerostin levels significantly increases heel bone mineral density and reduces the risk of osteoporosis, while showing no discernible effect on ischemic cardiovascular diseases. Quantile IV contributes to the advancement of MR methodology, and the case study on the impact of circulating sclerostin modulation contributes to our understanding of the on-target effects of sclerostin inhibition.
2024-01-31
medRxiv (preprint)
doi.org
Piecewise Linear Parametrization of Policies: Towards Interpretable Deep Reinforcement Learning
Maxime Wabartha
Joelle Pineau
Learning inherently interpretable policies is a central challenge in the path to developing autonomous agents that humans can trust. Linear … (see more)policies can justify their decisions while interacting in a dynamic environment, but their reduced expressivity prevents them from solving hard tasks. Instead, we argue for the use of piecewise-linear policies. We carefully study to what extent they can retain the interpretable properties of linear policies while reaching competitive performance with neural baselines. In particular, we propose the HyperCombinator (HC), a piecewise-linear neural architecture expressing a policy with a controllably small number of sub-policies. Each sub-policy is linear with respect to interpretable features, shedding light on the decision process of the agent without requiring an additional explanation model. We evaluate HC policies in control and navigation experiments, visualize the improved interpretability of the agent and highlight its trade-off with performance. Moreover, we validate that the restricted model class that the HyperCombinator belongs to is compatible with the algorithmic constraints of various reinforcement learning algorithms.
2024-01-16
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
Piecewise Linear Parametrization of Policies: Towards Interpretable Deep Reinforcement Learning
Maxime Wabartha
Joelle Pineau
2024-01-16
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
Piecewise Linear Parametrization of Policies: Towards Interpretable Deep Reinforcement Learning
Maxime Wabartha
Joelle Pineau
Learning inherently interpretable policies is a central challenge in the path to developing autonomous agents that humans can trust. We argu… (see more)e for the use of policies that are piecewise-linear. We carefully study to what extent they can retain the interpretable properties of linear policies while performing competitively with neural baselines. In particular, we propose the HyperCombinator (HC), a piecewise-linear neural architecture expressing a policy with a controllably small number of sub-policies. Each sub-policy is linear with respect to interpretable features, shedding light on the agent’s decision process without needing an additional explanation model. We evaluate HC policies in control and navigation experiments, visualize the improved interpretability of the agent and highlight its trade-off with performance.
2024-01-01
International Conference on Learning Representations (published)
openreview.net
openreview.net