Maxime Gasse

According to our database, Maxime Gasse authored at least 28 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Too Big to Fool: Resisting Deception in Language Models.

Mohammad Reza Samsami, Mats Leon Richter, Juan Rodriguez, Megh Thakkar, Sarath Chandar, Maxime Gasse

CoRR, 2024

The BrowserGym Ecosystem for Web Agent Research.

Thibault Le Sellier De Chezelles, Maxime Gasse, Alexandre Drouin, Massimo Caccia, Léo Boisvert, Megh Thakkar, Tom Marty, Rim Assouel, Sahar Omidi Shayegan, Lawrence Keunho Jang, Xing Han Lù, Ori Yoran, Dehan Kong, Frank F. Xu, Siva Reddy, Quentin Cappart, Graham Neubig, Ruslan Salakhutdinov, Nicolas Chapados, Alexandre Lacoste

CoRR, 2024

WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks.

Léo Boisvert, Megh Thakkar, Maxime Gasse, Massimo Caccia, Thibault Le Sellier De Chezelles, Quentin Cappart, Nicolas Chapados, Alexandre Lacoste, Alexandre Drouin

CoRR, 2024

WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?

Alexandre Drouin, Maxime Gasse, Massimo Caccia, Issam H. Laradji, Manuel Del Verme, Tom Marty, Léo Boisvert, Megh Thakkar, Quentin Cappart, David Vázquez, Nicolas Chapados, Alexandre Lacoste

CoRR, 2024

Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy.

Brice Rauby, Paul Xing, Jonathan Porée, Maxime Gasse, Jean Provost

CoRR, 2024

WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?

Alexandre Drouin, Maxime Gasse, Massimo Caccia, Issam H. Laradji, Manuel Del Verme, Tom Marty, David Vázquez, Nicolas Chapados, Alexandre Lacoste

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Using Confounded Data in Latent Model-Based Reinforcement Learning.

Maxime Gasse, Damien Grasset, Guillaume Gaudron, Pierre-Yves Oudeyer

Trans. Mach. Learn. Res., 2023

2022

Lookback for Learning to Branch.

Prateek Gupta, Elias Boutros Khalil, Didier Chételat, Maxime Gasse, Andrea Lodi, Yoshua Bengio, M. Pawan Kumar

Trans. Mach. Learn. Res., 2022

On generalized surrogate duality in mixed-integer nonlinear programming.

Benjamin Müller, Gonzalo Muñoz, Maxime Gasse, Ambros M. Gleixner, Andrea Lodi, Felipe Serrano

Math. Program., 2022

The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights.

Maxime Gasse, Quentin Cappart, Jonas Charfreitag, Laurent Charlin, Didier Chételat, Antonia Chmiela, Justin Dumouchelle, Ambros M. Gleixner, Aleksandr M. Kazachkov, Elias B. Khalil, Pawel Lichocki, Andrea Lodi, Miles Lubin, Chris J. Maddison, Christopher Morris, Dimitri J. Papageorgiou, Augustin Parjadis, Sebastian Pokutta, Antoine Prouvost, Lara Scavuzzo, Giulia Zarpellon, Linxin Yang, Sha Lai, Akang Wang, Xiaodong Luo, Xiang Zhou, Haohan Huang, Sheng Cheng Shao, Yuanming Zhu, Dong Zhang, Tao Quan, Zixuan Cao, Yang Xu, Zhewei Huang, Shuchang Zhou, Binbin Chen, Minggui He, Hao Hao, Zhiyu Zhang, Zhiwu An, Kun Mao

CoRR, 2022

Learning to Branch with Tree MDPs.

Lara Scavuzzo, Feng Yang Chen, Didier Chételat, Maxime Gasse, Andrea Lodi, Neil Yorke-Smith, Karen I. Aardal

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy.

Léo Milecki, Jonathan Porée, Hatim Belgharbi, Chloé Bourquin, Rafat Damseh, Patrick Delafontaine-Martel, Frédéric Lesage, Maxime Gasse, Jean Provost

IEEE Trans. Medical Imaging, 2021

Causal Reinforcement Learning using Observational and Interventional Data.

Maxime Gasse, Damien Grasset, Guillaume Gaudron, Pierre-Yves Oudeyer

CoRR, 2021

Ecole: A Library for Learning Inside MILP Solvers.

Antoine Prouvost, Justin Dumouchelle, Maxime Gasse, Didier Chételat, Andrea Lodi

CoRR, 2021

The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights.

Maxime Gasse, Simon Bowly, Quentin Cappart, Jonas Charfreitag, Laurent Charlin, Didier Chételat, Antonia Chmiela, Justin Dumouchelle, Ambros M. Gleixner, Aleksandr M. Kazachkov, Elias B. Khalil, Pawel Lichocki, Andrea Lodi, Miles Lubin, Chris J. Maddison, Christopher Morris, Dimitri J. Papageorgiou, Augustin Parjadis, Sebastian Pokutta, Antoine Prouvost, Lara Scavuzzo, Giulia Zarpellon, Linxin Yang, Sha Lai, Akang Wang, Xiaodong Luo, Xiang Zhou, Haohan Huang, Sheng Cheng Shao, Yuanming Zhu, Dong Zhang, Tao Quan, Zixuan Cao, Yang Xu, Zhewei Huang, Shuchang Zhou, Binbin Chen, Minggui He, Hao Hao, Zhiyu Zhang, Zhiwu An, Kun Mao

Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021

2020

Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers.

Antoine Prouvost, Justin Dumouchelle, Lara Scavuzzo, Maxime Gasse, Didier Chételat, Andrea Lodi

CoRR, 2020

Hybrid Models for Learning to Branch.

Prateek Gupta, Maxime Gasse, Elias B. Khalil, M. Pawan Kumar, Andrea Lodi, Yoshua Bengio

CoRR, 2020

Hybrid Models for Learning to Branch.

Prateek Gupta, Maxime Gasse, Elias B. Khalil, Pawan Kumar Mudigonda, Andrea Lodi, Yoshua Bengio

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On the Effectiveness of Two-Step Learning for Latent-Variable Models.

Y. Cem Sübakan, Maxime Gasse, Laurent Charlin

Proceedings of the 30th IEEE International Workshop on Machine Learning for Signal Processing, 2020

2019

Exact Combinatorial Optimization with Graph Convolutional Neural Networks.

Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

On the use of binary stochastic autoencoders for multi-label classification under the zero-one loss.

Denis Lecoeuche, Alex Aussem, Maxime Gasse

Proceedings of the INNS Conference on Big Data and Deep Learning 2018, 2018

2017

Probabilistic Graphical Model Structure Learning: Application to Multi-Label Classification. (Apprentissage de Structure de Modèles Graphiques Probabilistes: Application à la Classification Multi-Label).

Maxime Gasse

PhD thesis, 2017

2016

F-Measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets.

Maxime Gasse, Alex Aussem

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016

Identifying the irreducible disjoint factors of a multivariate probability distribution.

Maxime Gasse, Alex Aussem

Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

2015

On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property.

Maxime Gasse, Alexandre Aussem, Haytham Elghazel

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning.

Maxime Gasse, Alex Aussem, Haytham Elghazel

Expert Syst. Appl., 2014

Analysis of risk factors of hip fracture with causal Bayesian networks.

Alex Aussem, Pascal Caillet, Zara Klemm, Maxime Gasse, Anne-Marie Schott, Michel Ducher

Proceedings of the International Work-Conference on Bioinformatics and Biomedical Engineering, 2014

2012

An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning.

Maxime Gasse, Alex Aussem, Haytham Elghazel

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012