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

Collaborating Alumni - McGill University
Co-supervisor :
Collaborating Alumni - McGill University
PhD - McGill University
Co-supervisor :
PhD - McGill University
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Master's Research - McGill University
Principal supervisor :
Master's Research - McGill University
Co-supervisor :
Master's Research - McGill University
Master's Research - McGill University
PhD - McGill University
Collaborating Alumni - McGill University
Collaborating Alumni - McGill University
PhD - McGill University
Principal supervisor :
Master's Research - McGill University
Master's Research - McGill University
Principal supervisor :
Collaborating researcher - McGill University
Co-supervisor :
PhD - McGill University
Principal supervisor :
Research Intern - McGill University
Master's Research - McGill University
Co-supervisor :
Research Intern - McGill University
PhD - McGill University
Principal supervisor :
PhD - McGill University
Collaborating Alumni - McGill University
Co-supervisor :

Publications

Prediction of Cell Type Specific Transcription Factor Binding Site Occupancy
Editorial on Special Issue on Probabilistic Models for Biomedical Image Analysis.
Manuel Jorge Cardoso
William M. Wells III
Albert C. S. Chung
Bayesian and grAphical Models for Biomedical Imaging
M. Jorge Cardoso
Ivor J. A. Simpson
Arbel, Tal
Annemie Ribbens
Assessing Generalization in TD methods for Deep Reinforcement Learning
Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces
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.