Portrait de Maxime Gasse

Maxime Gasse

Membre industriel associé
Professeur associé, Polytechnique Montréal, Département de génie informatique et génie logiciel
Chercheur scientifique principal, ServiceNow

Biographie

Je suis chercheur principal chez ServiceNow à Montréal, où je fais de la recherche à l'intersection de l'inférence causale et de l'apprentissage par renforcement. Je suis professeur adjoint à Polytechnique Montréal et membre associé de Mila – Institut québécois d’intelligence artificielle.

Je suis fasciné par la question de l'intelligence artificielle : pouvons-nous construire des machines qui pensent? Je crois humblement que nos tentatives de concevoir des machines pensantes peuvent être un chemin vers une compréhension fondamentale de l'intelligence et de nous-mêmes. Actuellement, je m'intéresse à la question consistant à savoir si et comment les idées du domaine de la causalité peuvent contribuer à la conception d'agents d'apprentissage autonomes. Je suis à la recherche de stagiaires motivé·e·s, doté·e·s de solides compétences techniques et d'une expérience dans l'apprentissage par renforcement et/ou la causalité.

Étudiants actuels

Doctorat - Polytechnique

Publications

WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?
Massimo Caccia
Issam Hadj Laradji
Manuel Del Verme
Tom Marty
Léo Boisvert
Megh Thakkar
David Vazquez
Alexandre Lacoste
WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
Massimo Caccia
Issam Hadj Laradji
Manuel Del Verme
Tom Marty
Léo Boisvert
Megh Thakkar
David Vazquez
Alexandre Lacoste
We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on measuri… (voir plus)ng the agents' ability to perform tasks that span the typical daily work of knowledge workers utilizing enterprise software systems. To this end, we propose WorkArena, a remote-hosted benchmark of 29 tasks based on the widely-used ServiceNow platform. We also introduce BrowserGym, an environment for the design and evaluation of such agents, offering a rich set of actions as well as multimodal observations. Our empirical evaluation reveals that while current agents show promise on WorkArena, there remains a considerable gap towards achieving full task automation. Notably, our analysis uncovers a significant performance disparity between open and closed-source LLMs, highlighting a critical area for future exploration and development in the field.
WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?
Massimo Caccia
Issam Hadj Laradji
Manuel Del Verme
Tom Marty
Léo Boisvert
Megh Thakkar
David Vazquez
Alexandre Lacoste
We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on measuri… (voir plus)ng the agents' ability to perform tasks that span the typical daily work of knowledge workers utilizing enterprise software systems. To this end, we propose WorkArena, a remote-hosted benchmark of 29 tasks based on the widely-used ServiceNow platform. We also introduce BrowserGym, an environment for the design and evaluation of such agents, offering a rich set of actions as well as multimodal observations. Our empirical evaluation reveals that while current agents show promise on WorkArena, there remains a considerable gap towards achieving full task automation. Notably, our analysis uncovers a significant performance disparity between open and closed-source LLMs, highlighting a critical area for future exploration and development in the field.
Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy
Brice Rauby
Paul Xing
Jonathan Por'ee
Jean Provost
The Unsolved Challenges of LLMs as Generalist Web Agents: A Case Study
Rim Assouel
Tom Marty
Massimo Caccia
Issam Hadj Laradji
Sai Rajeswar
Hector Palacios
David Vazquez
Alexandre Lacoste
Using Confounded Data in Latent Model-Based Reinforcement Learning
Damien GRASSET
Guillaume Gaudron
Pierre-Yves Oudeyer
Lookback for Learning to Branch
Prateek Gupta
Elias Boutros Khalil
Didier Chételat
M. Pawan Kumar
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Simon Bowly
Jonas Charfreitag
Didier Chételat
Antonia Chmiela
Justin Dumouchelle
Ambros Gleixner
Aleksandr Kazachkov
Elias Boutros Khalil
Paweł Lichocki
Miles Lubin
Chris J. Maddison
Christopher Morris
D. Papageorgiou
Augustin Parjadis
Sebastian Pokutta
Antoine Prouvost … (voir 22 de plus)
Lara Scavuzzo
Giulia Zarpellon
Linxin Yangm
Sha Lai
Akang Wang
Xiaodong Luo
Xiang Zhou
Haohan Huang
Sheng Cheng Shao
Yuanming Zhu
Dong Dong Zhang
Tao Manh Quan
Zixuan Cao
Yang Xu
Zhewei Huang
Shuchang Zhou
C. Binbin
He Minggui
Haoren Ren Hao
Zhang Zhiyu
An Zhiwu
Mao Kun
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused … (voir plus)on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning as a new approach for solving combinatorial problems, either directly as solvers or by enhancing exact solvers. Based on this context, the ML4CO aims at improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components. The competition featured three challenging tasks: finding the best feasible solution, producing the tightest optimality certificate, and giving an appropriate solver configuration. Three realistic datasets were considered: balanced item placement, workload apportionment, and maritime inventory routing. This last dataset was kept anonymous for the contestants.
On generalized surrogate duality in mixed-integer nonlinear programming
Benjamin Muller
Gonzalo Munoz
Ambros Gleixner
Felipe Serrano
On the Effectiveness of Two-Step Learning for Latent-Variable Models
Latent-variable generative models offer a principled solution for modeling and sampling from complex probability distributions. Implementing… (voir plus) a joint training objective with a complex prior, however, can be a tedious task, as one is typically required to derive and code a specific cost function for each new type of prior distribution. In this work, we propose a general framework for learning latent variable generative models in a two-step fashion. In the first step of the framework, we train an autoencoder, and in the second step we fit a prior model on the resulting latent distribution. This two-step approach offers a convenient alternative to joint training, as it allows for a straightforward combination of existing models without the hustle of deriving new cost functions, and the need for coding the joint training objectives. Through a set of experiments, we demonstrate that two-step learning results in performances similar to joint training, and in some cases even results in more accurate modeling.
On generalized surrogate duality in mixed-integer nonlinear programming
Benjamin Muller
Gonzalo Munoz
Ambros Gleixner
Felipe Serrano
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
Didier Chételat
Nicola Ferroni
Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm. We propose a new graph convolutional neural netw… (voir plus)ork model for learning branch-and-bound variable selection policies, which leverages the natural variable-constraint bipartite graph representation of mixed-integer linear programs. We train our model via imitation learning from the strong branching expert rule, and demonstrate on a series of hard problems that our approach produces policies that improve upon state-of-the-art machine-learning methods for branching and generalize to instances significantly larger than seen during training. Moreover, we improve for the first time over expert-designed branching rules implemented in a state-of-the-art solver on large problems. Code for reproducing all the experiments can be found at this https URL.