Doina Precup

Mohammad Sami Nur Islam Islam

Will Hua

Maîtrise recherche - McGill

Co-superviseur⋅e :

Doctorat - McGill

Doctorat - McGill

Doctorat - McGill

Maîtrise recherche - McGill

Arushi Jain

Doctorat - McGill

Maîtrise recherche - UdeM

Superviseur⋅e principal⋅e :

Doctorat - McGill

Postdoctorat - McGill

Elaine Lau

Maîtrise recherche - McGill

Jonathan Lebensold

Collaborateur·rice alumni - McGill

Sitao Luan

Doctorat - McGill

Ray Luo

Doctorat - McGill

Superviseur⋅e principal⋅e :

G McCracken

Doctorat - McGill

Shahrad Mohammadzadeh

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e :

Gabriela Moisescu-Pareja

Maîtrise recherche - McGill

Girdhar Neil Girdhar

Collaborateur·rice de 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

Janarthanan Rajendran

Collaborateur·rice alumni - UdeM

Superviseur⋅e principal⋅e :

Sarath Chandar

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

Keyu Wang

Stagiaire de recherche - McGill

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

Steve Wen

Baccalauréat - McGill

Co-superviseur⋅e :

Gregory Dudek

Shuyuan Zhang

Doctorat - McGill

Harry Zhao

Doctorat - McGill

Co-superviseur⋅e :

Billets de blogue

Generic thumbnail for Mila Blog articles.

22 février 2024

par

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

Lire l'article

Publications

Training Matters: Unlocking Potentials of Deeper Graph Convolutional Neural Networks

Sitao Luan

Mingde Zhao

Xiao-Wen Chang

2024-02-20

Complex Networks & Their Applications XII (publié)

When Do We Need Graph Neural Networks for Node Classification?

Sitao Luan

Chenqing Hua

Qincheng Lu

Jiaqi Zhu

Xiao-Wen Chang

2024-02-20

Complex Networks & Their Applications XII (publié)

Gintare Karolina Dziugaite

Mixtures of Experts Unlock Parameter Scaling for Deep RL

Johan Samir Obando Ceron

Ghada Sokar

Timon Willi

Clare Lyle

Jesse Farebrother

Jakob Nicolaus Foerster

Pablo Samuel Castro

2024-02-13

ArXiv (prépublication)

Gintare Karolina Dziugaite

Mixtures of Experts Unlock Parameter Scaling for Deep RL

Johan Samir Obando Ceron

Ghada Sokar

Timon Willi

Clare Lyle

Jesse Farebrother

Jakob Nicolaus Foerster

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… (voir plus)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.

2024-02-13

ArXiv (prépublication)

Gintare Karolina Dziugaite

Mixtures of Experts Unlock Parameter Scaling for Deep RL

Johan Samir Obando Ceron

Ghada Sokar

Timon Willi

Clare Lyle

Jesse Farebrother

Jakob Nicolaus Foerster

Pablo Samuel Castro

2024-02-13

ArXiv (prépublication)

On the Privacy of Selection Mechanisms with Gaussian Noise

Jonathan Lebensold

Borja Balle

2024-02-09

ArXiv (prépublication)

Code as Reward: Empowering Reinforcement Learning with VLMs

David Venuto

Mohammad 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… (voir plus)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.

2024-02-07

ArXiv (prépublication)

Effective Protein-Protein Interaction Exploration with PPIretrieval

Chenqing Hua

Connor Coley

Guy Wolf

Shuangjia Zheng

2024-02-06

ArXiv (prépublication)

Effective Protein-Protein Interaction Exploration with PPIretrieval

Chenqing Hua

Connor W. Coley

Guy Wolf

Shuangjia Zheng

Protein-protein interactions (PPIs) are crucial in regulating numerous cellular functions, including signal transduction, transportation, an… (voir plus)d immune defense. As the accuracy of multi-chain protein complex structure prediction improves, the challenge has shifted towards effectively navigating the vast complex universe to identify potential PPIs. Herein, we propose PPIretrieval, the first deep learning-based model for protein-protein interaction exploration, which leverages existing PPI data to effectively search for potential PPIs in an embedding space, capturing rich geometric and chemical information of protein surfaces. When provided with an unseen query protein with its associated binding site, PPIretrieval effectively identifies a potential binding partner along with its corresponding binding site in an embedding space, facilitating the formation of protein-protein complexes.

2024-02-06

ArXiv (prépublication)

Effective Protein-Protein Interaction Exploration with PPIretrieval

Chenqing Hua

Connor W. Coley

Guy Wolf

Shuangjia Zheng

2024-02-06

ArXiv (prépublication)

Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning

Harry Zhao 0001

Harry Zhao

Mingde Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Yoshua Bengio

Inspired by human conscious planning, we propose Skipper, a model-based reinforcement learning framework utilizing spatio-temporal abstracti… (voir plus)ons to generalize better in novel situations. It automatically decomposes the given task into smaller, more manageable subtasks, and thus enables sparse decision-making and focused computation on the relevant parts of the environment. The decomposition relies on the extraction of an abstracted proxy problem represented as a directed graph, in which vertices and edges are learned end-to-end from hindsight. Our theoretical analyses provide performance guarantees under appropriate assumptions and establish where our approach is expected to be helpful. Generalization-focused experiments validate Skipper’s significant advantage in zero-shot generalization, compared to some existing state-of-the-art hierarchical planning methods.

2024-01-16

ICLR.cc/2024/Conference (poster)

openreview.net

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

Haque Ishfaq

Qingfeng Lan

Pan Xu

A. Rupam Mahmood

Animashree Anandkumar

Kamyar Azizzadenesheli

We present a scalable and effective exploration strategy based on Thompson sampling for reinforcement learning (RL). One of the key shortcom… (voir plus)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

2024-01-16

ICLR.cc/2024/Conference (poster)