Portrait de Doina Precup

Doina Precup

Membre académique principal
precupdo@mila.quebec
Chaire en IA Canada-CIFAR
Professeure agrégée, McGill University, École d'informatique
Chef d'équipe de recherche, Google DeepMind
Sujets de recherche
Apprentissage automatique médical
Apprentissage par renforcement
Modèles probabilistes
Modélisation moléculaire
Raisonnement

Biographie

Doina Precup enseigne à l'Université McGill tout en menant des recherches fondamentales sur l'apprentissage par renforcement, notamment les applications de l'IA dans des domaines ayant des répercussions sociales, tels que les soins de santé. Elle s'intéresse à la prise de décision automatique dans des situations d'incertitude élevée.

Elle est membre de l'Institut canadien de recherches avancées (CIFAR) et de l'Association pour l'avancement de l'intelligence artificielle (AAAI), et dirige le bureau montréalais de DeepMind.

Ses spécialités sont les suivantes : intelligence artificielle, apprentissage machine, apprentissage par renforcement, raisonnement et planification sous incertitude, applications.

Étudiants actuels

Safa Alver
Doctorat - McGill
alversaf@mila.quebec
Samin Yeasar Arnob
Doctorat - McGill
Site web
Google Scholar
Sumana Basu
Doctorat - McGill
Co-superviseur⋅e :
Adriana Romero Soriano
Site web
Github
Google Scholar
Veronica Chelu
Doctorat - McGill
Github
Lynn Cherif
Maîtrise recherche - McGill
Co-superviseur⋅e :
Khimya Khetarpal
Site web
Google Scholar
Raymond Chua
Doctorat - McGill
Co-superviseur⋅e :
Blake Richards
Site web
Google Scholar
Wesley Chung
Doctorat - McGill
Superviseur⋅e principal⋅e :
David Meger
Jonathan Colaço Carr
Maîtrise recherche - McGill
Superviseur⋅e principal⋅e :
Prakash Panangaden
Élodie Coté-Gauthier
Stagiaire de recherche - McGill
Derek Kweku DEGBEDZUI Degbedzui
Maîtrise recherche - McGill
Github
Google Scholar
Franco Del Balso
Stagiaire de recherche - UdeM
Github
Manuel Del Verme
Doctorat - McGill
Jesse Farebrother
Doctorat - McGill
Superviseur⋅e principal⋅e :
Marc Gendron-Bellemare
Site web
Github
Google Scholar
Tugce Gurbuz
Doctorat - McGill
Superviseur⋅e principal⋅e :
Eilif Benjamin Muller
Site web
Github
Google Scholar
Howard Huang
Doctorat - McGill
Haque Ishfaq
Doctorat - McGill
Site web
Github
Google Scholar
Mohammad Sami Nur Islam Islam
Maîtrise recherche - McGill
Github
Google Scholar
Arushi Jain
Doctorat - McGill
Site web
Github
Google Scholar
Thomas Jiralerspong
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Yoshua Bengio
Site web
Github
Google Scholar
Martin Klissarov
Doctorat - McGill
Site web
Github
Google Scholar
Flemming Kondrup
Postdoctorat - McGill
Google Scholar
Elaine Lau
Maîtrise recherche - McGill
Jonathan Lebensold
Collaborateur·rice alumni - McGill
Site web
Github
Google Scholar
Ray Luo
Doctorat - McGill
Superviseur⋅e principal⋅e :
Xujie Si
Site web
Github
Google Scholar
G McCracken
Doctorat - McGill
Google Scholar
Nazanin Mohammadi Sepahvand
Doctorat - McGill
Shahrad Mohammadzadeh
Maîtrise recherche - McGill
Superviseur⋅e principal⋅e :
Reihaneh Rabbany
Site web
Github
Google Scholar
Gabriela Moisescu-Pareja
Maîtrise recherche - McGill
Girdhar Neil Girdhar
Collaborateur·rice de recherche - McGill
Padideh Nouri
Doctorat - UdeM
Co-superviseur⋅e :
Yoshua Bengio
Site web
Github
Google Scholar
Charles Onu
Doctorat - McGill
Site web
Github
Google Scholar
Gandharv Patil
Doctorat - McGill
Co-superviseur⋅e :
Pablo Samuel Castro
Site web
Github
Google Scholar
Nate Rahn
Doctorat - McGill
Superviseur⋅e principal⋅e :
Marc Gendron-Bellemare
Sahand Rezaei-Shoshtari
Doctorat - McGill
Co-superviseur⋅e :
David Meger
Site web
Github
Google Scholar
Mandana Samiei
Doctorat - McGill
Co-superviseur⋅e :
Blake Richards
samiemandana@gmail.com
Site web
Github
Google Scholar
Manoosh Samiei
Doctorat - McGill
Co-superviseur⋅e :
Paul Masset
Site web
Github
Google Scholar
Shishir Sharma
Doctorat - McGill
Site web
Github
Google Scholar
Nishanth Anand Vemgal
Doctorat - McGill
Site web
Github
Google Scholar
David Anthony Venuto
Doctorat - McGill
Priyesh Vijayan
Doctorat - McGill
Co-superviseur⋅e :
Samira Ebrahimi Kahou
Site web
Github
Google Scholar
Guangyuan Wang
Stagiaire de recherche - McGill
Steve Wen
Baccalauréat - McGill
Co-superviseur⋅e :
Gregory Dudek
Zijing Wu
Collaborateur·rice de recherche - McGill
Co-superviseur⋅e :
Paul Masset
Site web
Github
Google Scholar
Shuyuan Zhang
Doctorat - McGill
Github
Harry Zhao
Doctorat - McGill
Co-superviseur⋅e :
Yoshua Bengio
Site web
Github
Google Scholar

Publications

Effective Protein-Protein Interaction Exploration with PPIretrieval
Chenqing Hua
Connor W. Coley
Guy Wolf
Doina Precup
Shuangjia Zheng
Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, an… (voir plus)d immune defense. As the accuracy of multi-chain protein complex structure prediction improves, the challenge has shifted towards effectively navigating the vast complex universe to identify potential PPIs. Herein, we propose PPIretrieval, the first deep learning-based model for protein-protein interaction exploration, which leverages existing PPI data to effectively search for potential PPIs in an embedding space, capturing rich geometric and chemical information of protein surfaces. When provided with an unseen query protein with its associated binding site, PPIretrieval effectively identifies a potential binding partner along with its corresponding binding site in an embedding space, facilitating the formation of protein-protein complexes.
2024-02-06
ArXiv (prépublication)
doi.org
arxiv.org
Effective Protein-Protein Interaction Exploration with PPIretrieval
Chenqing Hua
Connor W. Coley
Guy Wolf
Doina Precup
Shuangjia Zheng
Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, an… (voir plus)d immune defense. As the accuracy of multi-chain protein complex structure prediction improves, the challenge has shifted towards effectively navigating the vast complex universe to identify potential PPIs. Herein, we propose PPIretrieval, the first deep learning-based model for protein-protein interaction exploration, which leverages existing PPI data to effectively search for potential PPIs in an embedding space, capturing rich geometric and chemical information of protein surfaces. When provided with an unseen query protein with its associated binding site, PPIretrieval effectively identifies a potential binding partner along with its corresponding binding site in an embedding space, facilitating the formation of protein-protein complexes.
2024-02-06
ArXiv (prépublication)
doi.org
arxiv.org
Effective Protein-Protein Interaction Exploration with PPIretrieval
Chenqing Hua
Connor Coley
Guy Wolf
Doina Precup
Shuangjia Zheng
2024-02-06
ArXiv (prépublication)
doi.org
arxiv.org
Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning
Harry Zhao 0001
Harry Zhao
Mingde Zhao
Safa Alver
Harm van Seijen
Romain Laroche
Doina Precup
Yoshua Bengio
Inspired by human conscious planning, we propose Skipper, a model-based reinforcement learning framework utilizing spatio-temporal abstracti… (voir plus)ons to generalize better in novel situations. It automatically decomposes the given task into smaller, more manageable subtasks, and thus enables sparse decision-making and focused computation on the relevant parts of the environment. The decomposition relies on the extraction of an abstracted proxy problem represented as a directed graph, in which vertices and edges are learned end-to-end from hindsight. Our theoretical analyses provide performance guarantees under appropriate assumptions and establish where our approach is expected to be helpful. Generalization-focused experiments validate Skipper’s significant advantage in zero-shot generalization, compared to some existing state-of-the-art hierarchical planning methods.
2024-01-16
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
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… (voir plus)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
Connecting Weighted Automata, Tensor Networks and Recurrent Neural Networks through Spectral Learning
Tianyu Li
Doina Precup
Guillaume Rabusseau
2024-01-01
Mach. Learn. (publié)
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… (voir plus)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 (prépublication)
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… (voir plus)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 (prépublication)
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… (voir plus)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 (prépublication)
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… (voir plus)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 (prépublication)
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… (voir plus)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 (prépublication)
doi.org
arxiv.org