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 :

Research Intern - McGill University

william.potter@mila.quebec

manoosh.samiei@mila.quebec

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

PhD - McGill University

Nishanth Anand Vemgal

PhD - McGill University

PhD - McGill University

Co-supervisor :

Samira Ebrahimi Kahou

Research Intern - McGill University

guangyuan.wang@mila.quebec

Zihan Wang

PhD - McGill University

zihan.wang@mila.quebec

shuyuan.zhang@mila.quebec

Steve Wen

Master's Research - McGill University

Co-supervisor :

Gregory Dudek

steve.wen@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

Mitigating Downstream Model Risks via Model Provenance

Keyu Wang

Abdullah Norozi Iranzad

Scott Schaffter

Jonathan Lebensold

Meg Risdal

Research and industry are rapidly advancing the innovation and adoption of foundation model-based systems, yet the tools for managing these … (see more)models have not kept pace. Understanding the provenance and lineage of models is critical for researchers, industry, regulators, and public trust. While model cards and system cards were designed to provide transparency, they fall short in key areas: tracing model genealogy, enabling machine readability, offering reliable centralized management systems, and fostering consistent creation incentives. This challenge mirrors issues in software supply chain security, but AI/ML remains at an earlier stage of maturity. Addressing these gaps requires industry-standard tooling that can be adopted by foundation model publishers, open-source model innovators, and major distribution platforms. We propose a machine-readable model specification format to simplify the creation of model records, thereby reducing error-prone human effort, notably when a new model inherits most of its design from a foundation model. Our solution explicitly traces relationships between upstream and downstream models, enhancing transparency and traceability across the model lifecycle. To facilitate the adoption, we introduce the unified model record (UMR) repository , a semantically versioned system that automates the publication of model records to multiple formats (PDF, HTML, LaTeX) and provides a hosted web interface (https://modelrecord.com/). This proof of concept aims to set a new standard for managing foundation models, bridging the gap between innovation and responsible model management.

2024-10-08

SoLaR @ Neural Information Processing Systems (poster)

An Attentive Approach for Building Partial Reasoning Agents from Pixels

Safa Alver

We study the problem of building reasoning agents that are able to generalize in an effective manner. Towards this goal, we propose an end-t… (see more)o-end approach for building model-based reinforcement learning agents that dynamically focus their reasoning to the relevant aspects of the environment: after automatically identifying the distinct aspects of the environment, these agents dynamically filter out the relevant ones and then pass them to their simulator to perform partial reasoning. Unlike existing approaches, our approach works with pixel-based inputs and it allows for interpreting the focal points of the agent. Our quantitative analyses show that the proposed approach allows for effective generalization in high-dimensional domains with raw observational inputs. We also perform ablation analyses to validate our design choices. Finally, we demonstrate through qualitative analyses that our approach actually allows for building agents that focus their reasoning on the relevant aspects of the environment.

2024-09-16

TMLR (accepted)

Functional Acceleration for Policy Mirror Descent

Veronica Chelu

2024-07-31

EWRL/2024/Workshop (accepted)

Understanding Decision-Time vs. Background Planning in Model-Based Reinforcement Learning

Safa Alver

In model-based reinforcement learning, an agent can leverage a learned model to improve its way of behaving in different ways. Two prevalent… (see more) approaches are decision-time planning and background planning. In this study, we are interested in understanding under what conditions and in which settings one of these two planning styles will perform better than the other in domains that require fast responses. After viewing them through the lens of dynamic programming, we first consider the classical instantiations of these planning styles and provide theoretical results and hypotheses on which one will perform better in the pure planning, planning&learning, and transfer learning settings. We then consider the modern instantiations of these planning styles and provide hypotheses on which one will perform better in the last two of the considered settings. Lastly, we perform several illustrative experiments to empirically validate both our theoretical results and hypotheses. Overall, our findings suggest that even though decision-time planning does not perform as well as background planning in their classical instantiations, in their modern instantiations, it can perform on par or better than background planning in both the planning&learning and transfer learning settings.

2024-07-31

EWRL/2024/Workshop (accepted)

Code as Reward: Empowering Reinforcement Learning with VLMs

David Venuto

Sami Nur Islam

Martin Klissarov

Sherry Yang

Ankit Anand

Pre-trained Vision-Language Models (VLMs) are able to understand visual concepts, describe and decompose complex tasks into sub-tasks, and p… (see more)rovide feedback on task completion. In this paper, we aim to leverage these capabilities to support the training of reinforcement learning (RL) agents. In principle, VLMs are well suited for this purpose, as they can naturally analyze image-based observations and provide feedback (reward) on learning progress. However, inference in VLMs is computationally expensive, so querying them frequently to compute rewards would significantly slowdown the training of an RL agent. To address this challenge, we propose a framework named Code as Reward (VLM-CaR). VLM-CaR produces dense reward functions from VLMs through code generation, thereby significantly reducing the computational burden of querying the VLM directly. We show that the dense rewards generated through our approach are very accurate across a diverse set of discrete and continuous environments, and can be more effective in training RL policies than the original sparse environment rewards.

2024-07-22

International Conference on Machine Learning (Accept (Spotlight))

Mixtures of Experts Unlock Parameter Scaling for Deep RL

Johan Obando-Ceron

Ghada Sokar

Timon Willi

Clare Lyle

Jesse Farebrother

Jakob Foerster

Karolina Dziugaite

Pablo Samuel Castro

The recent rapid progress in (self) supervised learning models is in large part predicted by empirical scaling laws: a model's performance s… (see more)cales proportionally to its size. Analogous scaling laws remain elusive for reinforcement learning domains, however, where increasing the parameter count of a model often hurts its final performance. In this paper, we demonstrate that incorporating Mixture-of-Expert (MoE) modules, and in particular Soft MoEs (Puigcerver et al., 2023), into value-based networks results in more parameter-scalable models, evidenced by substantial performance increases across a variety of training regimes and model sizes. This work thus provides strong empirical evidence towards developing scaling laws for reinforcement learning.

2024-07-22

International Conference on Machine Learning (Accept (Spotlight))

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)

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)

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))

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))