Doina Precup

Samin Yeasar Arnob

PhD - McGill University

arnobsam@mila.quebec

sumana.basu@mail.mcgill.ca

Sumana Basu

Collaborating Alumni - McGill University

Co-supervisor :

Adriana Romero Soriano

Master's Research - McGill University

gunbir-singh.baveja@mila.quebec

Collaborating Alumni - McGill University

cheluver@mila.quebec

Raymond Chua

PhD - McGill University

Co-supervisor :

PhD - McGill University

Principal supervisor :

David Meger

chungwes@mila.quebec

Jonathan Colaço Carr

Master's Research - McGill University

Principal supervisor :

Prakash Panangaden

jonathan.colaco-carr@mila.quebec

Élodie Coté-Gauthier

Master's Research - McGill University

Co-supervisor :

Isabeau Prémont-Schwarz

elodie.cote-gauthier@mila.quebec

Franco Del Balso

Master's Research - McGill University

franco.delbalso@mila.quebec

busra-tugce.gurbuz@mila.quebec

Jesse Farebrother

PhD - McGill University

Principal supervisor :

Marc Gendron-Bellemare

PhD - McGill University

Principal supervisor :

Eilif B. Muller

Mohammad Sami Nur Islam Islam

Jonathan Hu

Master's Research - McGill University

jonathan.hu@mila.quebec

Howard Huang

PhD - McGill University

huanghow@mila.quebec

Master's Research - McGill University

mohammad-sami-nur.islam@mila.quebec

Hangzhan Jin

PhD - Polytechnique Montréal

jinhangzhan@gmail.com

flemming.kondrup@mila.quebec

Flemming Kondrup

Postdoctorate - McGill University

Jonathan Lebensold

Collaborating Alumni - McGill University

Collaborating Alumni - McGill University

jeremy.lucas@mila.quebec

Ray Luo

PhD - McGill University

Principal supervisor :

Xujie Si

luo.ziyan@mila.quebec

Sicheng Lyu

Master's Research - McGill University

sicheng.lyu@mila.quebec

G McCracken

PhD - McGill University

mccrackg@mila.quebec

shahrad.mohammadzadeh@mila.quebec

Shahrad Mohammadzadeh

Master's Research - McGill University

Principal supervisor :

Reihaneh Rabbany

padideh.nouri@mila.quebec

Gabriela Moisescu-Pareja

Collaborating researcher - McGill University

Co-supervisor :

Irina Rish

moisescg@mila.quebec

Padideh Nouri

PhD - Université de Montréal

PhD - McGill University

Co-supervisor :

Nate Rahn

PhD - McGill University

Principal supervisor :

Marc Gendron-Bellemare

nathan.rahn@mila.quebec

Mandana Samiei

PhD - McGill University

Co-supervisor :

Blake Richards

samiemandana@gmail.com

PhD - McGill University

Co-supervisor :

Paul Masset

manoosh.samiei@mila.quebec

PhD - McGill University

Nishanth Anand Vemgal

PhD - McGill University

PhD - McGill University

Co-supervisor :

Samira Ebrahimi Kahou

Zihan Wang

PhD - McGill University

zihan.wang@mila.quebec

guangyuan.wang@mila.quebec

Guangyuan Wang

Research Intern - McGill University

Steve Wen

Master's Research - McGill University

Co-supervisor :

Gregory Dudek

steve.wen@mila.quebec

Will William

Research Intern - McGill University

william.potter@mila.quebec

shuyuan.zhang@mila.quebec

Zijing Wu

PhD - McGill University

Principal supervisor :

Paul Masset

zijing.wu@mila.quebec

PhD - McGill University

Harry Zhao

Collaborating Alumni - McGill University

Co-supervisor :

Blog Posts

Generic thumbnail for Mila Blog articles.

February 22, 2024

Skipper: Combining Spatial and Temporal Abstraction for Better Generalization

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

Read the article

Publications

The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges

Sitao Luan

Chenqing Hua

Qincheng Lu

Liheng Ma

Lirong Wu

Xinyu Wang

Minkai Xu

Xiao-Wen Chang

Rex Ying

Stan Z. Li

Jian Tang

Guy Wolf

Stefanie Jegelka

Homophily principle, \ie{} nodes with the same labels or similar attributes are more likely to be connected, has been commonly believed to b… (see more)e the main reason for the superiority of Graph Neural Networks (GNNs) over traditional Neural Networks (NNs) on graph-structured data, especially on node-level tasks. However, recent work has identified a non-trivial set of datasets where GNN's performance compared to the NN's is not satisfactory. Heterophily, i.e. low homophily, has been considered the main cause of this empirical observation. People have begun to revisit and re-evaluate most existing graph models, including graph transformer and its variants, in the heterophily scenario across various kinds of graphs, e.g. heterogeneous graphs, temporal graphs and hypergraphs. Moreover, numerous graph-related applications are found to be closely related to the heterophily problem. In the past few years, considerable effort has been devoted to studying and addressing the heterophily issue. In this survey, we provide a comprehensive review of the latest progress on heterophilic graph learning, including an extensive summary of benchmark datasets and evaluation of homophily metrics on synthetic graphs, meticulous classification of the most updated supervised and unsupervised learning methods, thorough digestion of the theoretical analysis on homophily/heterophily, and broad exploration of the heterophily-related applications. Notably, through detailed experiments, we are the first to categorize benchmark heterophilic datasets into three sub-categories: malignant, benign and ambiguous heterophily. Malignant and ambiguous datasets are identified as the real challenging datasets to test the effectiveness of new models on the heterophily challenge. Finally, we propose several challenges and future directions for heterophilic graph representation learning.

2024-07-11

arXiv (Cornell University) (preprint)

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

Côme Fiegel

Andrea Michi

Marco Selvi

Sertan Girgin

Nikola Momchev

Olivier Bachem

Daniel J Mankowitz

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. Traditionally, RLHF involves the initial step of learning a reward model from pairwise human feedback, i.e., expressed as preferences between pairs of text generations. Subsequently, the LLM’s policy is fine-tuned to maximize the reward through a reinforcement learning algorithm. 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 pairwise preference model, which is conditioned on two inputs (instead of a single input in the case of a reward model) 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. We illustrate the effectiveness of our approach by presenting experimental results on a text summarization task. We believe NLHF offers a compelling avenue for fine-tuning LLMs and enhancing the alignment of LLMs with human preferences.

2024-07-07

Proceedings of the 41st International Conference on Machine Learning (published)

Recurrent Policies Are Not Enough for Continual Reinforcement Learning

Nathan Samuel de Lara

Veronica Chelu

Continual Reinforcement Learning (CRL) aims to develop algorithms that adapt to non-stationary sequences of tasks. A promising recent approa… (see more)ch utilizes Recurrent Neural Networks (RNNs) to learn contextual Markov Decision Process (MDP) embeddings. This enables a reinforcement learning (RL) agent to discern the optimality of actions across diverse tasks. In this study, we examine two critical failure modes in the learning of these contextual MDP embeddings. Specifically, we find that RNNs are prone to catastrophic forgetting, manifesting in two distinct ways: (i) embedding collapse---where agents initially learn a contextual task structure that later collapses to a single task, and (ii) embedding drift---where learning embeddings for new MDPs interferes with embeddings the RNN outputs for previous MDPs in the sequence, leading to suboptimal performance of downstream policy networks conditioned on stale embeddings. We explore the effects of various objective functions and network architectures concerning these failure modes, revealing that one of these modes consistently emerges across different setups.

2024-06-06

rl-conference.cc/RLC/2024/Workshop/ICBINB (poster)

openreview.net

More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling

Haque Ishfaq

Yixin Tan

Yu Yang

Qingfeng Lan

Jianfeng Lu

A. Rupam Mahmood

Pan Xu

2024-05-14

Reinforcement Learning Conference (published)

openreview.net

Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo

Haque Ishfaq

Qingfeng Lan

Pan Xu

A. Rupam Mahmood

Anima 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

2024-05-06

International Conference on Learning Representations (Accept (poster))

openreview.net

Conditions on Preference Relations that Guarantee the Existence of Optimal Policies

Jonathan Colaço Carr

Prakash Panangaden

Learning from Preferential Feedback (LfPF) plays an essential role in training Large Language Models, as well as certain types of interactiv… (see more)e learning agents. However, a substantial gap exists between the theory and application of LfPF algorithms. Current results guaranteeing the existence of optimal policies in LfPF problems assume that both the preferences and transition dynamics are determined by a Markov Decision Process. We introduce the Direct Preference Process, a new framework for analyzing LfPF problems in partially-observable, non-Markovian environments. Within this framework, we establish conditions that guarantee the existence of optimal policies by considering the ordinal structure of the preferences. We show that a decision-making problem can have optimal policies -- that are characterized by recursive optimality equations -- even when no reward function can express the learning goal. These findings underline the need to explore preference-based learning strategies which do not assume that preferences are generated by reward.

2024-05-01

International Conference on Artificial Intelligence and Statistics (Accept (Poster))

On learning history-based policies for controlling Markov decision processes

Gandharv Patil

Aditya Mahajan

Reinforcementlearning(RL)folkloresuggeststhathistory-basedfunctionapproximationmethods,suchas recurrent neural nets or history-based state a… (see more)bstraction, perform better than their memory-less counterparts, due to the fact that function approximation in Markov decision processes (MDP) can be viewed as inducing a Partially observable MDP. However, there has been little formal analysis of such history-based algorithms, as most existing frameworks focus exclusively on memory-less features. In this paper, we introduce a theoretical framework for studying the behaviour of RL algorithms that learn to control an MDP using history-based feature abstraction mappings. Furthermore, we use this framework to design a practical RL algorithm and we numerically evaluate its effectiveness on a set of continuous control tasks.

2024-05-01

International Conference on Artificial Intelligence and Statistics (Accept (Poster))

On the Privacy of Selection Mechanisms with Gaussian Noise

Jonathan Lebensold

Borja Balle

Report Noisy Max and Above Threshold are two classical differentially private (DP) selection mechanisms. Their output is obtained by adding … (see more)noise to a sequence of low-sensitivity queries and reporting the identity of the query whose (noisy) answer satisfies a certain condition. Pure DP guarantees for these mechanisms are easy to obtain when Laplace noise is added to the queries. On the other hand, when instantiated using Gaussian noise, standard analyses only yield approximate DP guarantees despite the fact that the outputs of these mechanisms lie in a discrete space. In this work, we revisit the analysis of Report Noisy Max and Above Threshold with Gaussian noise and show that, under the additional assumption that the underlying queries are bounded, it is possible to provide pure ex-ante DP bounds for Report Noisy Max and pure ex-post DP bounds for Above Threshold. The resulting bounds are tight and depend on closed-form expressions that can be numerically evaluated using standard methods. Empirically we find these lead to tighter privacy accounting in the high privacy, low data regime. Further, we propose a simple privacy filter for composing pure ex-post DP guarantees, and use it to derive a fully adaptive Gaussian Sparse Vector Technique mechanism. Finally, we provide experiments on mobility and energy consumption datasets demonstrating that our Sparse Vector Technique is practically competitive with previous approaches and requires less hyper-parameter tuning.

2024-05-01

International Conference on Artificial Intelligence and Statistics (Accept (Poster))

Discrete Probabilistic Inference as Control in Multi-path Environments

Nikolay Malkin

We consider the problem of sampling from a discrete and structured distribution as a sequential decision problem, where the objective is to … (see more)find a stochastic policy such that objects are sampled at the end of this sequential process proportionally to some predefined reward. While we could use maximum entropy Reinforcement Learning (MaxEnt RL) to solve this problem for some distributions, it has been shown that in general, the distribution over states induced by the optimal policy may be biased in cases where there are multiple ways to generate the same object. To address this issue, Generative Flow Networks (GFlowNets) learn a stochastic policy that samples objects proportionally to their reward by approximately enforcing a conservation of flows across the whole Markov Decision Process (MDP). In this paper, we extend recent methods correcting the reward in order to guarantee that the marginal distribution induced by the optimal MaxEnt RL policy is proportional to the original reward, regardless of the structure of the underlying MDP. We also prove that some flow-matching objectives found in the GFlowNet literature are in fact equivalent to well-established MaxEnt RL algorithms with a corrected reward. Finally, we study empirically the performance of multiple MaxEnt RL and GFlowNet algorithms on multiple problems involving sampling from discrete distributions.

2024-04-25

Conference on Uncertainty in Artificial Intelligence (poster)

CryCeleb: A Speaker Verification Dataset Based on Infant Cry Sounds

David Budaghyan

Arsenii Gorin

Cem Subakan

Charles C. Onu

This paper describes the Ubenwa CryCeleb dataset - a labeled collection of infant cries - and the accompanying CryCeleb 2023 task, which is … (see more)a public speaker verification challenge based on cry sounds. We released more than 6 hours of manually segmented cry sounds from 786 newborns for academic use, aiming to encourage research in infant cry analysis. The inaugural public competition attracted 59 participants, 11 of whom improved the baseline performance. The top-performing system achieved a significant improvement scoring 25.8% equal error rate, which is still far from the performance of state-of-the-art adult speaker verification systems. Therefore, we believe there is room for further research on this dataset, potentially extending beyond the verification task.

2024-04-13

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)

arxiv.org

Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks

Sitao Luan

Mingde Zhao

Xiao-Wen Chang

2024-02-19

Complex Networks & Their Applications XII (published)