Doina Precup

Sumana Basu

PhD - McGill University

Co-supervisor :

Adriana Romero Soriano

PhD - McGill University

Derek Kweku DEGBEDZUI Degbedzui

Lynn Cherif

Master's Research - McGill University

Co-supervisor :

PhD - McGill University

Co-supervisor :

PhD - McGill University

Principal supervisor :

David Meger

Jonathan Colaço Carr

Master's Research - McGill University

Principal supervisor :

Prakash Panangaden

Élodie Coté-Gauthier

Research Intern - McGill University

Master's Research - McGill University

Manuel Del Verme

PhD - McGill University

Jesse Farebrother

PhD - McGill University

Principal supervisor :

Marc Gendron-Bellemare

PhD - McGill University

Principal supervisor :

Eilif Benjamin Muller

Will Hua

Master's Research - McGill University

Co-supervisor :

Guy Wolf

Howard Huang

PhD - McGill University

Haque Ishfaq

PhD - McGill University

Mohammad Sami Nur Islam Islam

Master's Research - McGill University

Riashat Islam

PhD - McGill University

Arushi Jain

PhD - McGill University

Master's Research - Université de Montréal

Principal supervisor :

PhD - McGill University

Postdoctorate - McGill University

Elaine Lau

Master's Research - McGill University

Jonathan Lebensold

Collaborating Alumni - McGill University

Sitao Luan

PhD - McGill University

Ray Luo

PhD - McGill University

Principal supervisor :

G McCracken

PhD - McGill University

Shahrad Mohammadzadeh

Master's Research - McGill University

Principal supervisor :

Gabriela Moisescu-Pareja

Master's Research - McGill University

Girdhar Neil Girdhar

Collaborating researcher - McGill University

Padideh Nouri

PhD - Université de Montréal

Co-supervisor :

Charles Onu

PhD - McGill University

PhD - McGill University

Co-supervisor :

Nate Rahn

PhD - McGill University

Principal supervisor :

Marc Gendron-Bellemare

Janarthanan Rajendran

Collaborating Alumni - Université de Montréal

Principal supervisor :

Sarath Chandar

Sahand Rezaei-Shoshtari

PhD - McGill University

Co-supervisor :

PhD - McGill University

Co-supervisor :

Blake Richards

samiemandana@gmail.com

PhD - McGill University

Co-supervisor :

PhD - McGill University

Nishanth Anand Vemgal

PhD - McGill University

PhD - McGill University

Priyesh Vijayan

PhD - McGill University

Co-supervisor :

Samira Ebrahimi Kahou

Research Intern - McGill University

Keyu Wang

Research Intern - McGill University

Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

Steve Wen

Undergraduate - McGill University

Co-supervisor :

Gregory Dudek

Shuyuan Zhang

PhD - McGill University

Harry Zhao

PhD - McGill University

Co-supervisor :

Blog Posts

Generic thumbnail for Mila Blog articles.

February 22, 2024

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

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Publications

Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces.

Bogdan Mazoure

Thang Doan

Tianyu Li

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.

A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms

Philip Amortila

Prakash Panangaden

Marc Gendron-Bellemare

2020-01-01

AISTATS (published)

proceedings.mlr.press

Efficient Planning under Partial Observability with Unnormalized Q Functions and Spectral Learning

Tianyu Li

Bogdan Mazoure

Guillaume Rabusseau

Learning and planning in partially-observable domains is one of the most difficult problems in reinforcement learning. Traditional methods c… (see more)onsider these two problems as independent, resulting in a classical two-stage paradigm: first learn the environment dynamics and then plan accordingly. This approach, however, disconnects the two problems and can consequently lead to algorithms that are sample inefficient and time consuming. In this paper, we propose a novel algorithm that combines learning and planning together. Our algorithm is closely related to the spectral learning algorithm for predicitive state representations and offers appealing theoretical guarantees and time complexity. We empirically show on two domains that our approach is more sample and time efficient compared to classical methods.

2019-11-01

ArXiv (preprint)

Improving Pathological Structure Segmentation via Transfer Learning Across Diseases

Barleen Kaur

Paul Lemaitre

Raghav Mehta

Nazanin Mohammadi Sepahvand

Douglas Arnold

Tal Arbel

2019-10-13

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data (published)

Avoidance Learning Using Observational Reinforcement Learning

David Venuto

Léonard Boussioux

Junhao Wang

Rola Dali

Jhelum Chakravorty

Yoshua Bengio

Imitation learning seeks to learn an expert policy from sampled demonstrations. However, in the real world, it is often difficult to find a … (see more)perfect expert and avoiding dangerous behaviors becomes relevant for safety reasons. We present the idea of \textit{learning to avoid}, an objective opposite to imitation learning in some sense, where an agent learns to avoid a demonstrator policy given an environment. We define avoidance learning as the process of optimizing the agent's reward while avoiding dangerous behaviors given by a demonstrator. In this work we develop a framework of avoidance learning by defining a suitable objective function for these problems which involves the \emph{distance} of state occupancy distributions of the expert and demonstrator policies. We use density estimates for state occupancy measures and use the aforementioned distance as the reward bonus for avoiding the demonstrator. We validate our theory with experiments using a wide range of partially observable environments. Experimental results show that we are able to improve sample efficiency during training compared to state of the art policy optimization and safety methods.

2019-09-24

ArXiv (preprint)

Combined Reinforcement Learning via Abstract Representations

Vincent Francois-Lavet

Yoshua Bengio

Joelle Pineau

In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this p… (see more)aper we propose a new way of explicitly bridging both approaches via a shared low-dimensional learned encoding of the environment, meant to capture summarizing abstractions. We show that the modularity brought by this approach leads to good generalization while being computationally efficient, with planning happening in a smaller latent state space. In addition, this approach recovers a sufficient low-dimensional representation of the environment, which opens up new strategies for interpretable AI, exploration and transfer learning.

2019-07-17

Proceedings of the AAAI Conference on Artificial Intelligence (published)

Learning Options with Interest Functions

Khimya Khetarpal

Learning temporal abstractions which are partial solutions to a task and could be reused for solving other tasks is an ingredient that can h… (see more)elp agents to plan and learn efficiently. In this work, we tackle this problem in the options framework. We aim to autonomously learn options which are specialized in different state space regions by proposing a notion of interest functions, which generalizes initiation sets from the options framework for function approximation. We build on the option-critic framework to derive policy gradient theorems for interest functions, leading to a new interest-option-critic architecture.

2019-07-17

Proceedings of the AAAI Conference on Artificial Intelligence (published)

Leveraging Observations in Bandits: Between Risks and Benefits

Andrei-Stefan Lupu

Audrey Durand

Imitation learning has been widely used to speed up learning in novice agents, by allowing them to leverage existing data from experts. Allo… (see more)wing an agent to be influenced by external observations can benefit to the learning process, but it also puts the agent at risk of following sub-optimal behaviours. In this paper, we study this problem in the context of bandits. More specifically, we consider that an agent (learner) is interacting with a bandit-style decision task, but can also observe a target policy interacting with the same environment. The learner observes only the target’s actions, not the rewards obtained. We introduce a new bandit optimism modifier that uses conditional optimism contingent on the actions of the target in order to guide the agent’s exploration. We analyze the effect of this modification on the well-known Upper Confidence Bound algorithm by proving that it preserves a regret upper-bound of order O(lnT), even in the presence of a very poor target, and we derive the dependency of the expected regret on the general target policy. We provide empirical results showing both great benefits as well as certain limitations inherent to observational learning in the multi-armed bandit setting. Experiments are conducted using targets satisfying theoretical assumptions with high probability, thus narrowing the gap between theory and application.

2019-07-17

Proceedings of the AAAI Conference on Artificial Intelligence (published)

Prediction of Disease Progression in Multiple Sclerosis Patients using Deep Learning Analysis of MRI Data

Adrian Tousignant

Paul Lemaitre

Douglas Arnold

Tal Arbel

We present the first automatic end-to-end deep learning framework for the prediction of future patient disability progression (one year from… (see more) baseline) based on multi-modal brain Magnetic Resonance Images (MRI) of patients with Multiple Sclerosis (MS). The model uses parallel convolutional pathways, an idea introduced by the popular Inception net (Szegedy et al., 2015) and is trained and tested on two large proprietary, multi-scanner, multi-center, clinical trial datasets of patients with Relapsing-Remitting Multiple Sclerosis (RRMS). Experiments on 465 patients on the placebo arms of the trials indicate that the model can accurately predict future disease progression, measured by a sustained increase in the extended disability status scale (EDSS) score over time. Using only the multi-modal MRI provided at baseline, the model achieves an AUC of 0.66±0.055. However, when supplemental lesion label masks are provided as inputs as well, the AUC increases to 0.701± 0.027. Furthermore, we demonstrate that uncertainty estimates based on Monte Carlo dropout sample variance correlate with errors made by the model. Clinicians provided with the predictions computed by the model can therefore use the associated uncertainty estimates to assess which scans require further examination.

2019-05-24

International Conference on Medical Imaging with Deep Learning (published)

proceedings.mlr.press

Connecting Weighted Automata and Recurrent Neural Networks through Spectral Learning

Guillaume Rabusseau

Tianyu Li

In this paper, we unravel a fundamental connection between weighted finite automata~(WFAs) and second-order recurrent neural networks~(2-RNN… (see more)s): in the case of sequences of discrete symbols, WFAs and 2-RNNs with linear activation functions are expressively equivalent. Motivated by this result, we build upon a recent extension of the spectral learning algorithm to vector-valued WFAs and propose the first provable learning algorithm for linear 2-RNNs defined over sequences of continuous input vectors. This algorithm relies on estimating low rank sub-blocks of the so-called Hankel tensor, from which the parameters of a linear 2-RNN can be provably recovered. The performances of the proposed method are assessed in a simulation study.

2019-04-11

Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (published)

proceedings.mlr.press

Prediction of Progression in Multiple Sclerosis Patients

Adrian Tousignant

Paul Lemaitre

Douglas Arnold

Tal Arbel

We present the first automatic end-to-end deep learning framework for the prediction of future patient disability progression (one year from… (see more) baseline) based on multi-modal brain Magnetic Resonance Images (MRI) of patients with Multiple Sclerosis (MS). The model uses parallel convolutional pathways, an idea introduced by the popular Inception net and is trained and tested on two large proprietary, multi-scanner, multi-center, clinical trial datasets of patients with Relapsing-Remitting Multiple Sclerosis (RRMS). Experiments on 465 patients on the placebo arms of the trials indicate that the model can accurately predict future disease progression, measured by a sustained increase in the extended disability status scale (EDSS) score over time. Using only the multi-modal MRI provided at baseline, the model achieves an AUC of 0.66 +- 0.055. However, when supplemental lesion label masks are provided as inputs as well, the AUC increases to 0.701 +- 0.027. Furthermore, we demonstrate that uncertainty estimates based on Monte Carlo dropout sample variance correlate with errors made by the model. Clinicians provided with the predictions computed by the model can therefore use the associated uncertainty estimates to assess which scans require further examination.

2019-02-28

MIDL.io/2019/Conference (poster)

openreview.net

The Termination Critic

Anna Harutyunyan

Will Dabney

Diana L. Borsa

Nicolas Heess

Remi Munos

In this work, we consider the problem of autonomously discovering behavioral abstractions, or options, for reinforcement learning agents. We… (see more) propose an algorithm that focuses on the termination function, as opposed to - as is common - the policy. The termination function is usually trained to optimize a control objective: an option ought to terminate if another has better value. We offer a different, information-theoretic perspective, and propose that terminations should focus instead on the compressibility of the option’s encoding - arguably a key reason for using abstractions.To achieve this algorithmically, we leverage the classical options framework, and learn the option transition model as a “critic” for the termination function. Using this model, we derive gradients that optimize the desired criteria. We show that the resulting options are non-trivial, intuitively meaningful, and useful for learning.

2019-02-26

ArXiv (preprint)