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

Adaptive Exploration for Data-Efficient General Value Function Evaluations

Arushi Jain

Josiah P. Hanna

General Value Functions (GVFs) (Sutton et al., 2011) represent predictive knowledge in reinforcement learning. Each GVF computes the expecte… (see more)d return for a given policy, based on a unique reward. Existing methods relying on fixed behavior policies or pre-collected data often face data efficiency issues when learning multiple GVFs in parallel using off-policy methods. To address this, we introduce GVFExplorer, which adaptively learns a single behavior policy that efficiently collects data for evaluating multiple GVFs in parallel. Our method optimizes the behavior policy by minimizing the total variance in return across GVFs, thereby reducing the required environmental interactions. We use an existing temporal-difference-style variance estimator to approximate the return variance. We prove that each behavior policy update decreases the overall mean squared error in GVF predictions. We empirically show our method's performance in tabular and nonlinear function approximation settings, including Mujoco environments, with stationary and non-stationary reward signals, optimizing data usage and reducing prediction errors across multiple GVFs.

2024-12-10

Conference on Neural Information Processing Systems (Accept (poster))

Efficient Reinforcement Learning by Discovering Neural Pathways

Samin Yeasar Arnob

Riyasat Ohib

Sergey Plis

Amy Zhang

Alessandro Sordoni

2024-12-10

Conference on Neural Information Processing Systems (Accept (poster))

Offline Multitask Representation Learning for Reinforcement Learning

Raman Arora

Haque Ishfaq

Songtao Feng

Thanh Nguyen-Tang

Mengdi Wang

Ming Yin

We study offline multitask representation learning in reinforcement learning (RL), where a learner is provided with an offline dataset from … (see more)different tasks that share a common representation and is asked to learn the shared representation. We theoretically investigate offline multitask low-rank RL, and propose a new algorithm called MORL for offline multitask representation learning. Furthermore, we examine downstream RL in reward-free, offline and online scenarios, where a new task is introduced to the agent that shares the same representation as the upstream offline tasks. Our theoretical results demonstrate the benefits of using the learned representation from the upstream offline task instead of directly learning the representation of the low-rank model.

2024-12-10

Conference on Neural Information Processing Systems (Accept (poster))

Parseval Regularization for Continual Reinforcement Learning

Loss of plasticity, trainability loss, and primacy bias have been identified as issues arising when training deep neural networks on sequenc… (see more)es of tasks -- all referring to the increased difficulty in training on new tasks. We propose to use Parseval regularization, which maintains orthogonality of weight matrices, to preserve useful optimization properties and improve training in a continual reinforcement learning setting. We show that it provides significant benefits to RL agents on a suite of gridworld, CARL and MetaWorld tasks. We conduct comprehensive ablations to identify the source of its benefits and investigate the effect of certain metrics associated to network trainability including weight matrix rank, weight norms and policy entropy.

2024-12-10

Conference on Neural Information Processing Systems (Accept (poster))

QGFN: Controllable Greediness with Action Values

Stephen Zhewen Lu

Generative Flow Networks (GFlowNets; GFNs) are a family of energy-based generative methods for combinatorial objects, capable of generating … (see more)diverse and high-utility samples. However, consistently biasing GFNs towards producing high-utility samples is non-trivial. In this work, we leverage connections between GFNs and reinforcement learning (RL) and propose to combine the GFN policy with an action-value estimate,

2024-12-10

Neural Information Processing Systems (Accept (poster))

ReactZyme: A Benchmark for Enzyme-Reaction Prediction

Bozitao Zhong

Liang Hong

Shuangjia Zheng

Enzymes, with their specific catalyzed reactions, are necessary for all aspects of life, enabling diverse biological processes and adaptatio… (see more)ns. Predicting enzyme functions is essential for understanding biological pathways, guiding drug development, enhancing bioproduct yields, and facilitating evolutionary studies. Addressing the inherent complexities, we introduce a new approach to annotating enzymes based on their catalyzed reactions. This method provides detailed insights into specific reactions and is adaptable to newly discovered reactions, diverging from traditional classifications by protein family or expert-derived reaction classes. We employ machine learning algorithms to analyze enzyme reaction datasets, delivering a much more refined view on the functionality of enzymes. Our evaluation leverages the largest enzyme-reaction dataset to date, derived from the SwissProt and Rhea databases with entries up to January 8, 2024. We frame the enzyme-reaction prediction as a retrieval problem, aiming to rank enzymes by their catalytic ability for specific reactions. With our model, we can recruit proteins for novel reactions and predict reactions in novel proteins, facilitating enzyme discovery and function annotation (https://github.com/WillHua127/ReactZyme).

2024-12-10

Conference on Neural Information Processing Systems (Accept (Poster))

Learning Successor Features the Simple Way

Raymond Chua

Arna Ghosh

Christos Kaplanis

Blake Aaron Richards

In Deep Reinforcement Learning (RL), it is a challenge to learn representations that do not exhibit catastrophic forgetting or interference … (see more)in non-stationary environments. Successor Features (SFs) offer a potential solution to this challenge. However, canonical techniques for learning SFs from pixel-level observations often lead to representation collapse, wherein representations degenerate and fail to capture meaningful variations in the data. More recent methods for learning SFs can avoid representation collapse, but they often involve complex losses and multiple learning phases, reducing their efficiency. We introduce a novel, simple method for learning SFs directly from pixels. Our approach uses a combination of a Temporal-difference (TD) loss and a reward prediction loss, which together capture the basic mathematical definition of SFs. We show that our approach matches or outperforms existing SF learning techniques in both 2D (Minigrid), 3D (Miniworld) mazes and Mujoco, for both single and continual learning scenarios. As well, our technique is efficient, and can reach higher levels of performance in less time than other approaches. Our work provides a new, streamlined technique for learning SFs directly from pixel observations, with no pretraining required.

2024-12-09

Advances in Neural Information Processing Systems 37 (published)

Towards AI-designed genomes using a variational autoencoder

Natasha K. Dudek

Genomes encode elaborate networks of genes whose products must seamlessly interact to support living organisms. Humans’ capacity to unders… (see more)tand these biological systems is limited by their sheer size and complexity. In this article, we develop a proof of concept framework for training a machine learning (ML) algorithm to model bacterial genome composition. To achieve this, we create simplified representations of genomes in the form of binary vectors that indicate the encoded genes, henceforth referred to as genome vectors. A denoising variational autoencoder was trained to accept corrupted genome vectors, in which most genes had been masked, and reconstruct the original. The resulting model, DeepGenomeVector, effectively captures complex dependencies in genomic networks, as evaluated by both qualitative and quantitative metrics. An in-depth functional analysis of a generated genome vector shows that its encoded pathways are interconnected, near complete, and ecologically cohesive. On the test set, where the model’s ability to reconstruct uncorrupted genome vectors was evaluated, Area Under the Receiver Operating Curve (AUROC) and F1 scores of 0.98 and 0.83, respectively, support the model’s strong performance. This article showcases the power of ML approaches for synthetic biology and highlights the possibility that artifical intelligence agents may one day be able to design genomes that animate carbon-based cells.

2024-11-30

Proceedings of the Royal Society B: Biological Sciences (published)

Reaction-conditioned De Novo Enzyme Design with GENzyme

Chenqing Hua

Jiarui Lu

Yang Liu

Odin Zhang

Jian Tang

Rex Ying

Wengong Jin

Guy Wolf

Shuangjia Zheng

The introduction of models like RFDiffusionAA, AlphaFold3, AlphaProteo, and Chai1 has revolutionized protein structure modeling and interact… (see more)ion prediction, primarily from a binding perspective, focusing on creating ideal lock-and-key models. However, these methods can fall short for enzyme-substrate interactions, where perfect binding models are rare, and induced fit states are more common. To address this, we shift to a functional perspective for enzyme design, where the enzyme function is defined by the reaction it catalyzes. Here, we introduce \textsc{GENzyme}, a \textit{de novo} enzyme design model that takes a catalytic reaction as input and generates the catalytic pocket, full enzyme structure, and enzyme-substrate binding complex. \textsc{GENzyme} is an end-to-end, three-staged model that integrates (1) a catalytic pocket generation and sequence co-design module, (2) a pocket inpainting and enzyme inverse folding module, and (3) a binding and screening module to optimize and predict enzyme-substrate complexes. The entire design process is driven by the catalytic reaction being targeted. This reaction-first approach allows for more accurate and biologically relevant enzyme design, potentially surpassing structure-based and binding-focused models in creating enzymes capable of catalyzing specific reactions. We provide \textsc{GENzyme} code at https://github.com/WillHua127/GENzyme.

2024-11-09

ArXiv (preprint)

arxiv.org

Soft Condorcet Optimization for Ranking of General Agents

Marc Lanctot

Kate Larson

Michael Kaisers

Quentin Berthet

Ian Gemp

Manfred Diaz

Roberto-Rafael Maura-Rivero

Yoram Bachrach

Anna Koop

A common way to drive progress of AI models and agents is to compare their performance on standardized benchmarks. Comparing the performance… (see more) of general agents requires aggregating their individual performances across a potentially wide variety of different tasks. In this paper, we describe a novel ranking scheme inspired by social choice frameworks, called Soft Condorcet Optimization (SCO), to compute the optimal ranking of agents: the one that makes the fewest mistakes in predicting the agent comparisons in the evaluation data. This optimal ranking is the maximum likelihood estimate when evaluation data (which we view as votes) are interpreted as noisy samples from a ground truth ranking, a solution to Condorcet's original voting system criteria. SCO ratings are maximal for Condorcet winners when they exist, which we show is not necessarily true for the classical rating system Elo. We propose three optimization algorithms to compute SCO ratings and evaluate their empirical performance. When serving as an approximation to the Kemeny-Young voting method, SCO rankings are on average 0 to 0.043 away from the optimal ranking in normalized Kendall-tau distance across 865 preference profiles from the PrefLib open ranking archive. In a simulated noisy tournament setting, SCO achieves accurate approximations to the ground truth ranking and the best among several baselines when 59\% or more of the preference data is missing. Finally, SCO ranking provides the best approximation to the optimal ranking, measured on held-out test sets, in a problem containing 52,958 human players across 31,049 games of the classic seven-player game of Diplomacy.

2024-10-30

ArXiv (preprint)

arxiv.org

Effective Protein-Protein Interaction Exploration with PPIretrieval

Chenqing Hua

Connor W. Coley

Guy Wolf