Portrait de Doina Precup

Doina Precup

Membre académique principal
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

Doctorat - McGill
Doctorat - McGill
Co-superviseur⋅e :
Doctorat - McGill
Maîtrise recherche - McGill
Co-superviseur⋅e :
Doctorat - McGill
Co-superviseur⋅e :
Doctorat - McGill
Superviseur⋅e principal⋅e :
Maîtrise recherche - McGill
Superviseur⋅e principal⋅e :
Stagiaire de recherche - McGill
Stagiaire de recherche - UdeM
Doctorat - McGill
Doctorat - McGill
Superviseur⋅e principal⋅e :
Doctorat - McGill
Superviseur⋅e principal⋅e :
Doctorat - McGill
Maîtrise recherche - McGill
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :
Postdoctorat - McGill
Maîtrise recherche - McGill
Collaborateur·rice alumni - McGill
Doctorat - McGill
Superviseur⋅e principal⋅e :
Doctorat - McGill
Doctorat - McGill
Maîtrise recherche - McGill
Superviseur⋅e principal⋅e :
Maîtrise recherche - McGill
Collaborateur·rice de recherche - McGill
Doctorat - UdeM
Co-superviseur⋅e :
Doctorat - McGill
Co-superviseur⋅e :
Doctorat - McGill
Superviseur⋅e principal⋅e :
Doctorat - McGill
Co-superviseur⋅e :
Doctorat - McGill
Co-superviseur⋅e :
Doctorat - McGill
Doctorat - McGill
Co-superviseur⋅e :
Stagiaire de recherche - McGill
Baccalauréat - McGill
Co-superviseur⋅e :
Collaborateur·rice de recherche - McGill
Co-superviseur⋅e :
Doctorat - McGill
Doctorat - McGill
Co-superviseur⋅e :

Publications

Learning domain-invariant classifiers for infant cry sounds
Charles Onu
Hemanth K. Sheetha
Arsenii Gorin
MUDiff: Unified Diffusion for Complete Molecule Generation
Chenqing Hua
Sitao Luan
Minkai Xu
Zhitao Ying
Rex Ying
Jie Fu
Stefano Ermon
DGFN: Double Generative Flow Networks
Elaine Lau
Nikhil Murali Vemgal
Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search
Abbas Mehrabian
Hyunjik Kim
Nicolas Sonnerat
Matej Balog
Gheorghe Comanici
Tudor Berariu
Andrew Lee
Anian Ruoss
Anna Bulanova
Daniel Toyama
Sam Blackwell
Bernardino Romera Paredes
Petar Veličković
Laurent Orseau
Joonkyung Lee
Anurag Murty Naredla
Adam Zsolt Wagner
Forecaster: Towards Temporally Abstract Tree-Search Planning from Pixels
Thomas Jiralerspong
Flemming Kondrup
The ability to plan at many different levels of abstraction enables agents to envision the long-term repercussions of their decisions and th… (voir plus)us enables sample-efficient learning. This becomes particularly beneficial in complex environments from high-dimensional state space such as pixels, where the goal is distant and the reward sparse. We introduce Forecaster, a deep hierarchical reinforcement learning approach which plans over high-level goals leveraging a temporally abstract world model. Forecaster learns an abstract model of its environment by modelling the transitions dynamics at an abstract level and training a world model on such transition. It then uses this world model to choose optimal high-level goals through a tree-search planning procedure. It additionally trains a low-level policy that learns to reach those goals. Our method not only captures building world models with longer horizons, but also, planning with such models in downstream tasks. We empirically demonstrate Forecaster's potential in both single-task learning and generalization to new tasks in the AntMaze domain.
A cry for help: Early detection of brain injury in newborns
Charles Onu
Samantha Latremouille
Arsenii Gorin
Junhao Wang
Uchenna Ekwochi
P. Ubuane
O. Kehinde
Muhammad A. Salisu
Datonye Briggs
Hybrid Scattering Transform - Long Short-Term Memory Networks for Intrapartum Fetal Heart Rate Classification
"Derek Kweku DEGBEDZUI
Michael W Kuzniewicz
Marie-Coralie Cornet
Yvonne Wu
Heather Forquer
Lawrence Gerstley
Emily F. Hamilton
P. Warrick
Robert E. Kearney
This study assessed the early detection of the increased risk of hypoxic ischemic encephalopathy using raw fetal heart rate and its transfor… (voir plus)mation with scattering transform and a long short-term memory recurrent neural network. There was no significant difference between the two approaches. However, the use of scattering transform produced lower computational demands. Considering scalability to the large data in our database and computational efficiency, the experiments involving scattering transform coefficients will be selected to conduct subsequent experiments. Future works will address the limitations of this study, including the low model performance.
A Definition of Continual Reinforcement Learning
David Abel
Andre Barreto
Benjamin Van Roy
Hado van Hasselt
Satinder Singh
For SALE: State-Action Representation Learning for Deep Reinforcement Learning
Scott Fujimoto
Wei-Di Chang
Edward J. Smith
Shixiang Shane Gu
In the field of reinforcement learning (RL), representation learning is a proven tool for complex image-based tasks, but is often overlooked… (voir plus) for environments with low-level states, such as physical control problems. This paper introduces SALE, a novel approach for learning embeddings that model the nuanced interaction between state and action, enabling effective representation learning from low-level states. We extensively study the design space of these embeddings and highlight important design considerations. We integrate SALE and an adaptation of checkpoints for RL into TD3 to form the TD7 algorithm, which significantly outperforms existing continuous control algorithms. On OpenAI gym benchmark tasks, TD7 has an average performance gain of 276.7% and 50.7% over TD3 at 300k and 5M time steps, respectively, and works in both the online and offline settings.
Prediction and Control in Continual Reinforcement Learning
Nishanth Anand
Temporal difference (TD) learning is often used to update the estimate of the value function which is used by RL agents to extract useful po… (voir plus)licies. In this paper, we focus on value function estimation in continual reinforcement learning. We propose to decompose the value function into two components which update at different timescales: a _permanent_ value function, which holds general knowledge that persists over time, and a _transient_ value function, which allows quick adaptation to new situations. We establish theoretical results showing that our approach is well suited for continual learning and draw connections to the complementary learning systems (CLS) theory from neuroscience. Empirically, this approach improves performance significantly on both prediction and control problems.
Policy composition in reinforcement learning via multi-objective policy optimization
Shruti Mishra
Jordan Hoffmann
Nicolas Heess
Martin A. Riedmiller
Abbas Abdolmaleki
We enable reinforcement learning agents to learn successful behavior policies by utilizing relevant pre-existing teacher policies. The teach… (voir plus)er policies are introduced as objectives, in addition to the task objective, in a multi-objective policy optimization setting. Using the Multi-Objective Maximum a Posteriori Policy Optimization algorithm (Abdolmaleki et al. 2020), we show that teacher policies can help speed up learning, particularly in the absence of shaping rewards. In two domains with continuous observation and action spaces, our agents successfully compose teacher policies in sequence and in parallel, and are also able to further extend the policies of the teachers in order to solve the task. Depending on the specified combination of task and teacher(s), teacher(s) may naturally act to limit the final performance of an agent. The extent to which agents are required to adhere to teacher policies are determined by hyperparameters which determine both the effect of teachers on learning speed and the eventual performance of the agent on the task. In the humanoid domain (Tassa et al. 2018), we also equip agents with the ability to control the selection of teachers. With this ability, agents are able to meaningfully compose from the teacher policies to achieve a superior task reward on the walk task than in cases without access to the teacher policies. We show the resemblance of composed task policies with the corresponding teacher policies through videos.
Acceleration in Policy Optimization
Veronica Chelu
Tom Zahavy
Arthur Guez
Sebastian Flennerhag
We work towards a unifying paradigm for accelerating policy optimization methods in reinforcement learning (RL) through predictive and adapt… (voir plus)ive directions of (functional) policy ascent. Leveraging the connection between policy iteration and policy gradient methods, we view policy optimization algorithms as iteratively solving a sequence of surrogate objectives, local lower bounds on the original objective. We define optimism as predictive modelling of the future behavior of a policy, and hindsight adaptation as taking immediate and anticipatory corrective actions to mitigate accumulating errors from overshooting predictions or delayed responses to change. We use this shared lens to jointly express other well-known algorithms, including model-based policy improvement based on forward search, and optimistic meta-learning algorithms. We show connections with Anderson acceleration, Nesterov's accelerated gradient, extra-gradient methods, and linear extrapolation in the update rule. We analyze properties of the formulation, design an optimistic policy gradient algorithm, adaptive via meta-gradient learning, and empirically highlight several design choices pertaining to acceleration, in an illustrative task.