Maxime Gasse

39 papers A* 6B 1Journal 24Unranked 7
YearRankTypeTitle / Venue / Authors
2025 J jnl
Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy.
IEEE Trans. Image Process.
Brice Rauby, Paul Xing, Jonathan Porée, Maxime Gasse, Jean Provost
2025 J jnl
The BrowserGym Ecosystem for Web Agent Research.
Trans. Mach. Learn. Res.
Thibault Le Sellier de Chezelles, Maxime Gasse, Alexandre Lacoste, Massimo Caccia, Alexandre Drouin, 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, Graham Neubig, Quentin Cappart, Russ Salakhutdinov, Nicolas Chapados
2024 J jnl
Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy.
CoRR
Brice Rauby, Paul Xing, Jonathan Porée, Maxime Gasse, Jean Provost
2024 J jnl
The BrowserGym Ecosystem for Web Agent Research.
CoRR
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
2024 J jnl
Too Big to Fool: Resisting Deception in Language Models.
CoRR
Mohammad Reza Samsami, Mats Leon Richter, Juan A. Rodríguez, Megh Thakkar, Sarath Chandar, Maxime Gasse
2024 A* conf
WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks.
NeurIPS
Léo Boisvert, Megh Thakkar, Maxime Gasse, Massimo Caccia, Thibault Le Sellier de Chezelles, Quentin Cappart, Nicolas Chapados, Alexandre Lacoste, Alexandre Drouin
2024 J jnl
WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks.
CoRR
Léo Boisvert, Megh Thakkar, Maxime Gasse, Massimo Caccia, Thibault Le Sellier de Chezelles, Quentin Cappart, Nicolas Chapados, Alexandre Lacoste, Alexandre Drouin
2024 J jnl
WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
CoRR
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
2024 A* conf
WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?
ICML
Alexandre Drouin, Maxime Gasse, Massimo Caccia, Issam H. Laradji, Manuel Del Verme, Tom Marty, David Vázquez, Nicolas Chapados, Alexandre Lacoste
2023 J jnl
A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy.
CoRR
Léo Milecki, Jonathan Porée, Hatim Belgharbi, Chloé Bourquin, Rafat Damseh, Patrick Delafontaine-Martel, Frédéric Lesage, Maxime Gasse, Jean Provost
2023 J jnl
Using Confounded Data in Latent Model-Based Reinforcement Learning.
Trans. Mach. Learn. Res.
Maxime Gasse, Damien Grasset, Guillaume Gaudron, Pierre-Yves Oudeyer
2022 A* conf
Learning to Branch with Tree MDPs.
NeurIPS
Lara Scavuzzo, Feng Yang Chen, Didier Chételat, Maxime Gasse, Andrea Lodi, Neil Yorke-Smith, Karen I. Aardal
2022 J jnl
Learning to branch with Tree MDPs.
CoRR
Lara Scavuzzo, Feng Yang Chen, Didier Chételat, Maxime Gasse, Andrea Lodi, Neil Yorke-Smith, Karen I. Aardal
2022 J jnl
Lookback for Learning to Branch.
CoRR
Prateek Gupta, Elias B. Khalil, Didier Chételat, Maxime Gasse, Yoshua Bengio, Andrea Lodi, M. Pawan Kumar
2022 J jnl
Lookback for Learning to Branch.
Trans. Mach. Learn. Res.
Prateek Gupta, Elias Boutros Khalil, Didier Chételat, Maxime Gasse, Andrea Lodi, Yoshua Bengio, M. Pawan Kumar
2022 J jnl
On generalized surrogate duality in mixed-integer nonlinear programming.
Math. Program.
Benjamin Müller, Gonzalo Muñoz, Maxime Gasse, Ambros M. Gleixner, Andrea Lodi, Felipe Serrano
2022 J jnl
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights.
CoRR
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
2021 J jnl
A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy.
IEEE Trans. Medical Imaging
Léo Milecki, Jonathan Porée, Hatim Belgharbi, Chloé Bourquin, Rafat Damseh, Patrick Delafontaine-Martel, Frédéric Lesage, Maxime Gasse, Jean Provost
2021 J jnl
Causal Reinforcement Learning using Observational and Interventional Data.
CoRR
Maxime Gasse, Damien Grasset, Guillaume Gaudron, Pierre-Yves Oudeyer
2021 J jnl
Ecole: A Library for Learning Inside MILP Solvers.
CoRR
Antoine Prouvost, Justin Dumouchelle, Maxime Gasse, Didier Chételat, Andrea Lodi
2021 — conf
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights.
NeurIPS (Competition and Demos)
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
2020 J jnl
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers.
CoRR
Antoine Prouvost, Justin Dumouchelle, Lara Scavuzzo, Maxime Gasse, Didier Chételat, Andrea Lodi
2020 A* conf
Hybrid Models for Learning to Branch.
NeurIPS
Prateek Gupta, Maxime Gasse, Elias B. Khalil, Pawan Kumar Mudigonda, Andrea Lodi, Yoshua Bengio
2020 J jnl
Hybrid Models for Learning to Branch.
CoRR
Prateek Gupta, Maxime Gasse, Elias B. Khalil, M. Pawan Kumar, Andrea Lodi, Yoshua Bengio
2020 B conf
On Generalized Surrogate Duality in Mixed-Integer Nonlinear Programming.
IPCO
Benjamin Müller, Gonzalo Muñoz, Maxime Gasse, Ambros M. Gleixner, Andrea Lodi, Felipe Serrano
2020 — conf
On the Effectiveness of Two-Step Learning for Latent-Variable Models.
MLSP
Y. Cem Sübakan, Maxime Gasse, Laurent Charlin
2019 A* conf
Exact Combinatorial Optimization with Graph Convolutional Neural Networks.
NeurIPS
Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi
2019 J jnl
Exact Combinatorial Optimization with Graph Convolutional Neural Networks.
CoRR
Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi
2018 — conf
On the use of binary stochastic autoencoders for multi-label classification under the zero-one loss.
INNS Conference on Big Data
Denis Lecoeuche, Alex Aussem, Maxime Gasse
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
2016 — conf
F-Measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets.
ECML/PKDD (1)
Maxime Gasse, Alex Aussem
2016 J jnl
F-measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets.
CoRR
Maxime Gasse, Alex Aussem
2016 — conf
Identifying the irreducible disjoint factors of a multivariate probability distribution.
Probabilistic Graphical Models
Maxime Gasse, Alex Aussem
2015 J jnl
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning.
CoRR
Maxime Gasse, Alex Aussem, Haytham Elghazel
2015 J jnl
An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning.
CoRR
Maxime Gasse, Alex Aussem, Haytham Elghazel
2015 A* conf
On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property.
ICML
Maxime Gasse, Alexandre Aussem, Haytham Elghazel
2014 J jnl
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning.
Expert Syst. Appl.
Maxime Gasse, Alex Aussem, Haytham Elghazel
2014 — conf
Analysis of risk factors of hip fracture with causal Bayesian networks.
IWBBIO
Alex Aussem, Pascal Caillet, Zara Klemm, Maxime Gasse, Anne-Marie Schott, Michel Ducher
2012 — conf
An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning.
ECML/PKDD (1)
Maxime Gasse, Alex Aussem, Haytham Elghazel