Portrait of Doina Precup

Doina Precup

Core Academic Member
precupdo@mila.quebec
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

Samin Yeasar Arnob
PhD - McGill University
Website
Google Scholar
Sumana Basu
PhD - McGill University
Co-supervisor :
Adriana Romero Soriano
sumana.basu@mail.mcgill.ca
Website
Github
Google Scholar
Veronica Chelu
Collaborating Alumni - McGill University
Github
Lynn Cherif
Master's Research - McGill University
Co-supervisor :
Khimya Khetarpal
Website
Github
Google Scholar
Raymond Chua
PhD - McGill University
Co-supervisor :
Blake Richards
Website
Google Scholar
Wesley Chung
PhD - McGill University
Principal supervisor :
David Meger
Jonathan Colaço Carr
Master's Research - McGill University
Principal supervisor :
Prakash Panangaden
Élodie Coté-Gauthier
Collaborating researcher - McGill University
Github
Franco Del Balso
Research Intern - Université de Montréal
Github
Jesse Farebrother
PhD - McGill University
Principal supervisor :
Marc Gendron-Bellemare
Website
Github
Google Scholar
Tugce Gurbuz
PhD - McGill University
Principal supervisor :
Eilif Benjamin Muller
Website
Github
Google Scholar
Howard Huang
PhD - McGill University
Haque Ishfaq
Collaborating Alumni - McGill University
Website
Github
Google Scholar
Mohammad Sami Nur Islam Islam
Master's Research - McGill University
Github
Google Scholar
Arushi Jain
PhD - McGill University
Website
Github
Google Scholar
Flemming Kondrup
Postdoctorate - McGill University
Google Scholar
Elaine Lau
Master's Research - McGill University
Jonathan Lebensold
Collaborating Alumni - McGill University
Website
Github
Google Scholar
Jeremy Lucas
Undergraduate - McGill University
Ray Luo
PhD - McGill University
Principal supervisor :
Xujie Si
Website
Github
Google Scholar
G McCracken
PhD - McGill University
Google Scholar
Nazanin Mohammadi Sepahvand
Collaborating Alumni - McGill University
Github
Shahrad Mohammadzadeh
Master's Research - McGill University
Principal supervisor :
Reihaneh Rabbany
Website
Github
Google Scholar
Gabriela Moisescu-Pareja
Collaborating researcher - McGill University
Co-supervisor :
Irina Rish
Padideh Nouri
PhD - Université de Montréal
Co-supervisor :
Yoshua Bengio
Website
Github
Google Scholar
Gandharv Patil
PhD - McGill University
Co-supervisor :
Pablo Samuel Castro
Website
Github
Google Scholar
Nate Rahn
PhD - McGill University
Principal supervisor :
Marc Gendron-Bellemare
Sahand Rezaei-Shoshtari
PhD - McGill University
Co-supervisor :
David Meger
Website
Github
Google Scholar
Mandana Samiei
PhD - McGill University
Co-supervisor :
Blake Richards
samiemandana@gmail.com
Website
Github
Google Scholar
Manoosh Samiei
PhD - McGill University
Co-supervisor :
Paul Masset
Website
Github
Google Scholar
Shishir Sharma
PhD - McGill University
Website
Github
Google Scholar
Nishanth Anand Vemgal
PhD - McGill University
Website
Github
Google Scholar
Priyesh Vijayan
PhD - McGill University
Co-supervisor :
Samira Ebrahimi Kahou
Website
Github
Google Scholar
Zihan Wang
PhD - McGill University
Website
Google Scholar
Guangyuan Wang
Research Intern - McGill University
Github
Steve Wen
Master's Research - McGill University
Co-supervisor :
Gregory Dudek
Github
Zijing Wu
PhD - McGill University
Co-supervisor :
Paul Masset
Website
Github
Google Scholar
Shuyuan Zhang
PhD - McGill University
Github
Harry Zhao
Collaborating Alumni - McGill University
Co-supervisor :
Yoshua Bengio
Website
Github
Google Scholar

Publications

Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo
Haque Ishfaq
Qingfeng Lan
Pan Xu
A. Rupam Mahmood
Doina Precup
Animashree Anandkumar
Kamyar Azizzadenesheli
We present a scalable and effective exploration strategy based on Thompson sampling for reinforcement learning (RL). One of the key shortcom… (see more)ings of existing Thompson sampling algorithms is the need to perform a Gaussian approximation of the posterior distribution, which is not a good surrogate in most practical settings. We instead directly sample the Q function from its posterior distribution, by using Langevin Monte Carlo, an efficient type of Markov Chain Monte Carlo (MCMC) method. Our method only needs to perform noisy gradient descent updates to learn the exact posterior distribution of the Q function, which makes our approach easy to deploy in deep RL. We provide a rigorous theoretical analysis for the proposed method and demonstrate that, in the linear Markov decision process (linear MDP) setting, it has a regret bound of
2024-01-16
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
An Attentive Approach for Building Partial Reasoning Agents from Pixels
Safa Alver
Doina Precup
We study the problem of building reasoning agents that are able to generalize in an effective manner. Towards this goal, we propose an end-t… (see more)o-end approach for building model-based reinforcement learning agents that dynamically focus their reasoning to the relevant aspects of the environment: after automatically identifying the distinct aspects of the environment, these agents dynamically filter out the relevant ones and then pass them to their simulator to perform partial reasoning. Unlike existing approaches, our approach works with pixel-based inputs and it allows for interpreting the focal points of the agent. Our quantitative analyses show that the proposed approach allows for effective generalization in high-dimensional domains with raw observational inputs. We also perform ablation analyses to validate our design choices. Finally, we demonstrate through qualitative analyses that our approach actually allows for building agents that focus their reasoning on the relevant aspects of the environment.
2024-01-01
Trans. Mach. Learn. Res. (published)
openreview.net
openreview.net
Connecting Weighted Automata, Tensor Networks and Recurrent Neural Networks through Spectral Learning
Tianyu Li
Doina Precup
Guillaume Rabusseau
2024-01-01
Mach. Learn. (published)
doi.org
arxiv.org
Policy Gradient Methods in the Presence of Symmetries and State Abstractions
Prakash Panangaden
Sahand Rezaei-Shoshtari
Rosie Zhao
David Meger
Doina Precup
arxiv.org
Nash Learning from Human Feedback
Remi Munos
Michal Valko
Daniele Calandriello
Mohammad Gheshlaghi Azar
Mark Rowland
Zhaohan Daniel Guo
Yunhao Tang
Matthieu Geist
Thomas Mesnard
Andrea Michi
Marco Selvi
Sertan Girgin
Nikola Momchev
Olivier Bachem
Daniel J Mankowitz
Doina Precup
Bilal Piot
Reinforcement learning from human feedback (RLHF) has emerged as the main paradigm for aligning large language models (LLMs) with human pref… (see more)erences. Typically, RLHF involves the initial step of learning a reward model from human feedback, often expressed as preferences between pairs of text generations produced by a pre-trained LLM. Subsequently, the LLM's policy is fine-tuned by optimizing it to maximize the reward model through a reinforcement learning algorithm. However, an inherent limitation of current reward models is their inability to fully represent the richness of human preferences and their dependency on the sampling distribution. In this study, we introduce an alternative pipeline for the fine-tuning of LLMs using pairwise human feedback. Our approach entails the initial learning of a preference model, which is conditioned on two inputs given a prompt, followed by the pursuit of a policy that consistently generates responses preferred over those generated by any competing policy, thus defining the Nash equilibrium of this preference model. We term this approach Nash learning from human feedback (NLHF). In the context of a tabular policy representation, we present a novel algorithmic solution, Nash-MD, founded on the principles of mirror descent. This algorithm produces a sequence of policies, with the last iteration converging to the regularized Nash equilibrium. Additionally, we explore parametric representations of policies and introduce gradient descent algorithms for deep-learning architectures. To demonstrate the effectiveness of our approach, we present experimental results involving the fine-tuning of a LLM for a text summarization task. We believe NLHF offers a compelling avenue for preference learning and policy optimization with the potential of advancing the field of aligning LLMs with human preferences.
2023-12-01
ArXiv (preprint)
doi.org
arxiv.org
Nash Learning from Human Feedback
Remi Munos
Michal Valko
Daniele Calandriello
Mohammad Gheshlaghi Azar
Mark Rowland
Zhaohan Daniel Guo
Yunhao Tang
Matthieu Geist
Thomas Mesnard
Andrea Michi
Marco Selvi
Sertan Girgin
Nikola Momchev
Olivier Bachem
Daniel J Mankowitz
Doina Precup
Bilal Piot
Reinforcement learning from human feedback (RLHF) has emerged as the main paradigm for aligning large language models (LLMs) with human pref… (see more)erences. Typically, RLHF involves the initial step of learning a reward model from human feedback, often expressed as preferences between pairs of text generations produced by a pre-trained LLM. Subsequently, the LLM's policy is fine-tuned by optimizing it to maximize the reward model through a reinforcement learning algorithm. However, an inherent limitation of current reward models is their inability to fully represent the richness of human preferences and their dependency on the sampling distribution. In this study, we introduce an alternative pipeline for the fine-tuning of LLMs using pairwise human feedback. Our approach entails the initial learning of a preference model, which is conditioned on two inputs given a prompt, followed by the pursuit of a policy that consistently generates responses preferred over those generated by any competing policy, thus defining the Nash equilibrium of this preference model. We term this approach Nash learning from human feedback (NLHF). In the context of a tabular policy representation, we present a novel algorithmic solution, Nash-MD, founded on the principles of mirror descent. This algorithm produces a sequence of policies, with the last iteration converging to the regularized Nash equilibrium. Additionally, we explore parametric representations of policies and introduce gradient descent algorithms for deep-learning architectures. To demonstrate the effectiveness of our approach, we present experimental results involving the fine-tuning of a LLM for a text summarization task. We believe NLHF offers a compelling avenue for preference learning and policy optimization with the potential of advancing the field of aligning LLMs with human preferences.
2023-12-01
ArXiv (preprint)
doi.org
arxiv.org
Nash Learning from Human Feedback
Remi Munos
Michal Valko
Daniele Calandriello
Mohammad Gheshlaghi Azar
Mark Rowland
Zhaohan Daniel Guo
Yunhao Tang
Matthieu Geist
Thomas Mesnard
Andrea Michi
Marco Selvi
Sertan Girgin
Nikola Momchev
Olivier Bachem
Daniel J Mankowitz
Doina Precup
Bilal Piot
Reinforcement learning from human feedback (RLHF) has emerged as the main paradigm for aligning large language models (LLMs) with human pref… (see more)erences. Typically, RLHF involves the initial step of learning a reward model from human feedback, often expressed as preferences between pairs of text generations produced by a pre-trained LLM. Subsequently, the LLM's policy is fine-tuned by optimizing it to maximize the reward model through a reinforcement learning algorithm. However, an inherent limitation of current reward models is their inability to fully represent the richness of human preferences and their dependency on the sampling distribution. In this study, we introduce an alternative pipeline for the fine-tuning of LLMs using pairwise human feedback. Our approach entails the initial learning of a preference model, which is conditioned on two inputs given a prompt, followed by the pursuit of a policy that consistently generates responses preferred over those generated by any competing policy, thus defining the Nash equilibrium of this preference model. We term this approach Nash learning from human feedback (NLHF). In the context of a tabular policy representation, we present a novel algorithmic solution, Nash-MD, founded on the principles of mirror descent. This algorithm produces a sequence of policies, with the last iteration converging to the regularized Nash equilibrium. Additionally, we explore parametric representations of policies and introduce gradient descent algorithms for deep-learning architectures. To demonstrate the effectiveness of our approach, we present experimental results involving the fine-tuning of a LLM for a text summarization task. We believe NLHF offers a compelling avenue for preference learning and policy optimization with the potential of advancing the field of aligning LLMs with human preferences.
2023-12-01
ArXiv (preprint)
doi.org
arxiv.org
Nash Learning from Human Feedback
R'emi Munos
Michal Valko
Daniele Calandriello
M. G. Azar
Mark Rowland
Zhaohan Daniel Guo
Yunhao Tang
Matthieu Geist
Thomas Mesnard
Andrea Michi
Marco Selvi
Sertan Girgin
Nikola Momchev
Olivier Bachem
Daniel J Mankowitz
Doina Precup
Bilal Piot
Reinforcement learning from human feedback (RLHF) has emerged as the main paradigm for aligning large language models (LLMs) with human pref… (see more)erences. Typically, RLHF involves the initial step of learning a reward model from human feedback, often expressed as preferences between pairs of text generations produced by a pre-trained LLM. Subsequently, the LLM's policy is fine-tuned by optimizing it to maximize the reward model through a reinforcement learning algorithm. However, an inherent limitation of current reward models is their inability to fully represent the richness of human preferences and their dependency on the sampling distribution. In this study, we introduce an alternative pipeline for the fine-tuning of LLMs using pairwise human feedback. Our approach entails the initial learning of a preference model, which is conditioned on two inputs given a prompt, followed by the pursuit of a policy that consistently generates responses preferred over those generated by any competing policy, thus defining the Nash equilibrium of this preference model. We term this approach Nash learning from human feedback (NLHF). In the context of a tabular policy representation, we present a novel algorithmic solution, Nash-MD, founded on the principles of mirror descent. This algorithm produces a sequence of policies, with the last iteration converging to the regularized Nash equilibrium. Additionally, we explore parametric representations of policies and introduce gradient descent algorithms for deep-learning architectures. To demonstrate the effectiveness of our approach, we present experimental results involving the fine-tuning of a LLM for a text summarization task. We believe NLHF offers a compelling avenue for preference learning and policy optimization with the potential of advancing the field of aligning LLMs with human preferences.
2023-12-01
ArXiv (preprint)
doi.org
arxiv.org
Nash Learning from Human Feedback
Remi Munos
Michal Valko
Daniele Calandriello
Mohammad Gheshlaghi Azar
Mark Rowland
Zhaohan Daniel Guo
Yunhao Tang
Matthieu Geist
Thomas Mesnard
Andrea Michi
Marco Selvi
Sertan Girgin
Nikola Momchev
Olivier Bachem
Daniel J Mankowitz
Doina Precup
Bilal Piot
Reinforcement learning from human feedback (RLHF) has emerged as the main paradigm for aligning large language models (LLMs) with human pref… (see more)erences. Typically, RLHF involves the initial step of learning a reward model from human feedback, often expressed as preferences between pairs of text generations produced by a pre-trained LLM. Subsequently, the LLM's policy is fine-tuned by optimizing it to maximize the reward model through a reinforcement learning algorithm. However, an inherent limitation of current reward models is their inability to fully represent the richness of human preferences and their dependency on the sampling distribution. In this study, we introduce an alternative pipeline for the fine-tuning of LLMs using pairwise human feedback. Our approach entails the initial learning of a preference model, which is conditioned on two inputs given a prompt, followed by the pursuit of a policy that consistently generates responses preferred over those generated by any competing policy, thus defining the Nash equilibrium of this preference model. We term this approach Nash learning from human feedback (NLHF). In the context of a tabular policy representation, we present a novel algorithmic solution, Nash-MD, founded on the principles of mirror descent. This algorithm produces a sequence of policies, with the last iteration converging to the regularized Nash equilibrium. Additionally, we explore parametric representations of policies and introduce gradient descent algorithms for deep-learning architectures. To demonstrate the effectiveness of our approach, we present experimental results involving the fine-tuning of a LLM for a text summarization task. We believe NLHF offers a compelling avenue for preference learning and policy optimization with the potential of advancing the field of aligning LLMs with human preferences.
2023-12-01
ArXiv (preprint)
doi.org
arxiv.org
Nash Learning from Human Feedback
Remi Munos
Michal Valko
Daniele Calandriello
Mohammad Gheshlaghi Azar
Mark Rowland
Zhaohan Daniel Guo
Yunhao Tang
Matthieu Geist
Thomas Mesnard
Andrea Michi
Marco Selvi
Sertan Girgin
Nikola Momchev
Olivier Bachem
Daniel J Mankowitz
Doina Precup
Bilal Piot
Reinforcement learning from human feedback (RLHF) has emerged as the main paradigm for aligning large language models (LLMs) with human pref… (see more)erences. Typically, RLHF involves the initial step of learning a reward model from human feedback, often expressed as preferences between pairs of text generations produced by a pre-trained LLM. Subsequently, the LLM's policy is fine-tuned by optimizing it to maximize the reward model through a reinforcement learning algorithm. However, an inherent limitation of current reward models is their inability to fully represent the richness of human preferences and their dependency on the sampling distribution. In this study, we introduce an alternative pipeline for the fine-tuning of LLMs using pairwise human feedback. Our approach entails the initial learning of a preference model, which is conditioned on two inputs given a prompt, followed by the pursuit of a policy that consistently generates responses preferred over those generated by any competing policy, thus defining the Nash equilibrium of this preference model. We term this approach Nash learning from human feedback (NLHF). In the context of a tabular policy representation, we present a novel algorithmic solution, Nash-MD, founded on the principles of mirror descent. This algorithm produces a sequence of policies, with the last iteration converging to the regularized Nash equilibrium. Additionally, we explore parametric representations of policies and introduce gradient descent algorithms for deep-learning architectures. To demonstrate the effectiveness of our approach, we present experimental results involving the fine-tuning of a LLM for a text summarization task. We believe NLHF offers a compelling avenue for preference learning and policy optimization with the potential of advancing the field of aligning LLMs with human preferences.
2023-12-01
ArXiv (preprint)
doi.org
arxiv.org
Learning domain-invariant classifiers for infant cry sounds
Charles Onu
Hemanth K. Sheetha
Arsenii Gorin
Doina Precup
2023-11-30
ArXiv (preprint)
arxiv.org
arxiv.org
MUDiff: Unified Diffusion for Complete Molecule Generation
Chenqing Hua
Sitao Luan
Minkai Xu
Zhitao Ying
Rex Ying
Jie Fu
Stefano Ermon
Doina Precup
2023-11-18
logconference.io/LOG/2023/Conference (poster)
doi.org
openreview.net