Doina Precup

Mohammad Sami Nur Islam Islam

Jesse Farebrother

Doctorat - McGill

Superviseur⋅e principal⋅e :

Marc Gendron-Bellemare

Doctorat - McGill

Superviseur⋅e principal⋅e :

Eilif Benjamin Muller

Doctorat - McGill

Doctorat - McGill

Maîtrise recherche - McGill

Nazanin Mohammadi Sepahvand

Google Scholar

Arushi Jain

Doctorat - McGill

Doctorat - McGill

Postdoctorat - McGill

Google Scholar

Elaine Lau

Maîtrise recherche - McGill

Jonathan Lebensold

Collaborateur·rice alumni - McGill

Baccalauréat - McGill

Ray Luo

Doctorat - McGill

Superviseur⋅e principal⋅e :

G McCracken

Doctorat - McGill

Google Scholar

Doctorat - McGill

Shahrad Mohammadzadeh

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e :

Gabriela Moisescu-Pareja

Maîtrise recherche - McGill

Padideh Nouri

Doctorat - UdeM

Co-superviseur⋅e :

Charles Onu

Doctorat - McGill

Doctorat - McGill

Co-superviseur⋅e :

Nate Rahn

Doctorat - McGill

Superviseur⋅e principal⋅e :

Marc Gendron-Bellemare

Sahand Rezaei-Shoshtari

Doctorat - McGill

Co-superviseur⋅e :

Doctorat - McGill

Co-superviseur⋅e :

Doctorat - McGill

Co-superviseur⋅e :

Blake Richards

samiemandana@gmail.com

Doctorat - McGill

Nishanth Anand Vemgal

Doctorat - McGill

Doctorat - McGill

Doctorat - McGill

Co-superviseur⋅e :

Samira Ebrahimi Kahou

Stagiaire de recherche - McGill

Steve Wen

Maîtrise recherche - McGill

Co-superviseur⋅e :

Gregory Dudek

Skipper : combiner l’abstraction spatiale et temporelle afin d’améliorer la généralisation

Zijing Wu

Doctorat - McGill

Co-superviseur⋅e :

Doctorat - McGill

Harry Zhao

Doctorat - McGill

Co-superviseur⋅e :

Billets de blogue

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22 février 2024

par

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

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Publications

Temporal Abstraction in Reinforcement Learning with the Successor Representation

Marlos C. Machado

Andre Barreto

Michael Bowling

When Do Graph Neural Networks Help with Node Classification: Investigating the Homophily Principle on Node Distinguishability

Sitao Luan

Chenqing Hua

Minkai Xu

Qincheng Lu

Jiaqi Zhu

Xiao-Wen Chang

Jie Fu

Jure Leskovec

Homophily principle, i.e., nodes with the same labels are more likely to be connected, was believed to be the main reason for the performanc… (voir plus)e superiority of Graph Neural Networks (GNNs) over Neural Networks (NNs) on Node Classiﬁcation (NC) tasks. Recently, people have developed theoretical results arguing that, even though the homophily principle is broken, the advantage of GNNs can still hold as long as nodes from the same class share similar neighborhood patterns [29], which questions the validity of homophily. However, this argument only considers intra-class Node Distinguishability (ND) and ignores inter-class ND, which is insufﬁcient to study the effect of homophily. In this paper, we ﬁrst demonstrate the aforementioned insufﬁciency with examples and argue that an ideal situation for ND is to have smaller intra-class ND than inter-class ND. To formulate this idea and have a better understanding of homophily, we propose Contextual Stochastic Block Model for Homophily (CSBM-H) and deﬁne two metrics, Probabilistic Bayes Error (PBE) and Expected Negative KL-divergence (ENKL), to quantify ND, through which we can also ﬁnd how intra- and inter-class ND inﬂuence ND together. We visualize the results and give detailed analysis. Through experiments, we veriﬁed that the superiority of GNNs is

Offline Policy Optimization in RL with Variance Regularizaton

Riashat Islam

Samarth Sinha

Homanga Bharadhwaj

Samin Yeasar Arnob

Zhuoran Yang

Animesh Garg

Zhaoran Wang

Lihong Li

2022-12-29

ArXiv (prépublication)

Towards Continual Reinforcement Learning: A Review and Perspectives

Khimya Khetarpal

Matthew D Riemer

Irina Rish

2022-12-22

Journal of Artificial Intelligence Research (publié)

Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks

Sitao Luan

Harry Zhao

Mingde Zhao

Chenqing Hua

Xiao-Wen Chang

The core operation of current Graph Neural Networks (GNNs) is the aggregation enabled by the graph Laplacian or message passing, which filte… (voir plus)rs the neighborhood node information. Though effective for various tasks, in this paper, we show that they are potentially a problematic factor underlying all GNN methods for learning on certain datasets, as they force the node representations similar, making the nodes gradually lose their identity and become indistinguishable. Hence, we augment the aggregation operations with their dual, i.e. diversification operators that make the node more distinct and preserve the identity. Such augmentation replaces the aggregation with a two-channel filtering process that, in theory, is beneficial for enriching the node representations. In practice, the proposed two-channel filters can be easily patched on existing GNN methods with diverse training strategies, including spectral and spatial (message passing) methods. In the experiments, we observe desired characteristics of the models and significant performance boost upon the baselines on 9 node classification tasks.

2022-11-22

NeurIPS.cc/2022/Workshop/GLFrontiers (publié)

When Do We Need GNN for Node Classification?

Sitao Luan

Chenqing Hua

Qincheng Lu

Jiaqi Zhu

Xiao-Wen Chang

2022-10-30

arXiv.org (prépublication)

When Do We Need GNN for Node Classification?

Sitao Luan

Chenqing Hua

Qincheng Lu

Jiaqi Zhu

Xiao-Wen Chang

2022-10-30

arXiv.org (prépublication)

Low-Rank Representation of Reinforcement Learning Policies

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… (voir plus) 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 and convergence guarantees. We conduct several experiments on classic RL domains. The results confirm that the policies can be robustly represented in a low-dimensional space while the embedded policy incurs almost no decrease in returns.

2022-10-27

Journal of Artificial Intelligence Research (publié)

Simulating Human Gaze with Neural Visual Attention

Leo Schwinn

Bjoern Eskofier

Dario Zanca

2022-10-20

NeurIPS.cc/2022/Workshop/GMML (présentation orale)

The Paradox of Choice: On the Role of Attention in Hierarchical Reinforcement Learning

Andrei Cristian Nica

Khimya Khetarpal

Decision-making AI agents are often faced with two important challenges: the depth of the planning horizon, and the branching factor due to … (voir plus)having many choices. Hierarchical reinforcement learning methods aim to solve the first problem, by providing shortcuts that skip over multiple time steps. To cope with the breadth, it is desirable to restrict the agent's attention at each step to a reasonable number of possible choices. The concept of affordances (Gibson, 1977) suggests that only certain actions are feasible in certain states. In this work, we first characterize "affordances" as a "hard" attention mechanism that strictly limits the available choices of temporally extended options. We then investigate the role of hard versus soft attention in training data collection, abstract value learning in long-horizon tasks, and handling a growing number of choices. To this end, we present an online, model-free algorithm to learn affordances that can be used to further learn subgoal options. Finally, we identify and empirically demonstrate the settings in which the "paradox of choice" arises, i.e. when having fewer but more meaningful choices improves the learning speed and performance of a reinforcement learning agent.

2022-10-20

NeurIPS.cc/2022/Workshop/Attention (poster)

Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learning

Flemming Kondrup

Thomas Jiralerspong

Elaine Lau

Nathan de Lara

Jacob A. Shkrob

My Duc Tran

Sumana Basu

2022-10-05

ArXiv (prépublication)

Estimating individual treatment effect on disability progression in multiple sclerosis using deep learning

Jean-Pierre R. Falet

Joshua D. Durso-Finley

Brennan Nichyporuk

Julien Schroeter

Francesca Bovis

Maria-Pia Sormani

Tal Arbel

Douglas Arnold

2022-09-26

Nature Communications (publié)