Portrait of Doina Precup

Doina Precup

Core Academic Member
Canada CIFAR AI Chair
Associate Professor, McGill University, School of Computer Science
Research Team Leader, Google DeepMind
Research Topics
Medical Machine Learning
Molecular Modeling
Probabilistic Models
Reasoning
Reinforcement Learning

Biography

Doina Precup combines teaching at McGill University with fundamental research on reinforcement learning, in particular AI applications in areas of significant social impact, such as health care. She is interested in machine decision-making in situations where uncertainty is high.

In addition to heading the Montreal office of Google DeepMind, Precup is a Senior Fellow of the Canadian Institute for Advanced Research and a Fellow of the Association for the Advancement of Artificial Intelligence.

Her areas of speciality are artificial intelligence, machine learning, reinforcement learning, reasoning and planning under uncertainty, and applications.

Current Students

PhD - McGill University
PhD - McGill University
PhD - McGill University
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PhD - McGill University
Master's Research - McGill University
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PhD - McGill University
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PhD - McGill University
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Master's Research - McGill University
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Collaborating researcher - McGill University
Research Intern - Université de Montréal
PhD - McGill University
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PhD - McGill University
Principal supervisor :
PhD - McGill University
PhD - McGill University
Master's Research - McGill University
PhD - McGill University
PhD - McGill University
Postdoctorate - McGill University
Master's Research - McGill University
Collaborating Alumni - McGill University
Undergraduate - McGill University
PhD - McGill University
Principal supervisor :
PhD - McGill University
PhD - McGill University
Master's Research - McGill University
Principal supervisor :
Collaborating researcher - McGill University
Co-supervisor :
PhD - Université de Montréal
Co-supervisor :
PhD - McGill University
PhD - McGill University
Co-supervisor :
PhD - McGill University
Principal supervisor :
PhD - McGill University
Co-supervisor :
PhD - McGill University
Co-supervisor :
PhD - McGill University
PhD - McGill University
PhD - McGill University
Co-supervisor :
PhD - McGill University
Research Intern - McGill University
Master's Research - McGill University
Co-supervisor :
PhD - McGill University
Co-supervisor :
PhD - McGill University
Collaborating Alumni - McGill University
Co-supervisor :

Publications

Single-Shot Pruning for Offline Reinforcement Learning
Riyasat Ohib
Sergey Plis
Flexible Option Learning
Flexible Option Learning
Flexible Option Learning
Gradient Starvation: A Learning Proclivity in Neural Networks
We identify and formalize a fundamental gradient descent phenomenon resulting in a learning proclivity in over-parameterized neural networks… (see more). Gradient Starvation arises when cross-entropy loss is minimized by capturing only a subset of features relevant for the task, despite the presence of other predictive features that fail to be discovered. This work provides a theoretical explanation for the emergence of such feature imbalance in neural networks. Using tools from Dynamical Systems theory, we identify simple properties of learning dynamics during gradient descent that lead to this imbalance, and prove that such a situation can be expected given certain statistical structure in training data. Based on our proposed formalism, we develop guarantees for a novel regularization method aimed at decoupling feature learning dynamics, improving accuracy and robustness in cases hindered by gradient starvation. We illustrate our findings with simple and real-world out-of-distribution (OOD) generalization experiments.
Estimating treatment effect for individuals with progressive multiple sclerosis using deep learning
JR Falet
Joshua D. Durso-Finley
Jan Schroeter
Francesca Bovis
Maria-Pia Sormani
Douglas Arnold
Reward is enough
David Silver
Satinder Singh
Richard S. Sutton
Reward is enough
David Silver
Satinder Singh
Richard S. Sutton
Reward is enough
David Silver
Satinder Singh
Richard S. Sutton
Reward is enough
David Silver
Satinder Singh
Richard S. Sutton
A Survey of Exploration Methods in Reinforcement Learning
Susan Amin
Maziar Gomrokchi
Harsh Satija
Herke van Hoof
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor Representation
Scott Fujimoto
Marginalized importance sampling (MIS), which measures the density ratio between the state-action occupancy of a target policy and that of a… (see more) sampling distribution, is a promising approach for off-policy evaluation. However, current state-of-the-art MIS methods rely on complex optimization tricks and succeed mostly on simple toy problems. We bridge the gap between MIS and deep reinforcement learning by observing that the density ratio can be computed from the successor representation of the target policy. The successor representation can be trained through deep reinforcement learning methodology and decouples the reward optimization from the dynamics of the environment, making the resulting algorithm stable and applicable to high-dimensional domains. We evaluate the empirical performance of our approach on a variety of challenging Atari and MuJoCo environments.