Maxime Gasse

ORCID

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 26

Top Venues

CoRR

NeurIPS

Trans. Mach. Learn. Res.

IPCO

Claude Bernard University Lyon 1 (PHD)

Education History

Claude Bernard University Lyon 1

PHD

Publications

Léo Boisvert, Megh Thakkar, Maxime Gasse, Massimo Caccia, Thibault Le Sellier De Chezelles, Quentin Cappart, Nicolas Chapados, Alexandre Lacoste, Alexandre Drouin
WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks. CoRR (2024)
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
WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks? CoRR (2024)
Brice Rauby, Paul Xing, Jonathan Porée, Maxime Gasse, Jean Provost
Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy. CoRR (2024)
Léo Milecki, Jonathan Porée, Hatim Belgharbi, Chloé Bourquin, Rafat Damseh, Patrick Delafontaine-Martel, Frédéric Lesage, Maxime Gasse, Jean Provost
A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy. CoRR (2023)
Maxime Gasse, Damien Grasset, Guillaume Gaudron, Pierre-Yves Oudeyer
Using Confounded Data in Latent Model-Based Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023)
Lara Scavuzzo, Feng Yang Chen, Didier Chételat, Maxime Gasse, Andrea Lodi, Neil Yorke-Smith, Karen Aardal
Learning to Branch with Tree MDPs. NeurIPS (2022)
Benjamin Müller, Gonzalo Muñoz, Maxime Gasse, Ambros M. Gleixner, Andrea Lodi, Felipe Serrano
On generalized surrogate duality in mixed-integer nonlinear programming. Math. Program. 192 (1) (2022)
Prateek Gupta, Elias B. Khalil, Didier Chételat, Maxime Gasse, Yoshua Bengio, Andrea Lodi, M. Pawan Kumar
Lookback for Learning to Branch. CoRR (2022)
Lara Scavuzzo, Feng Yang Chen, Didier Chételat, Maxime Gasse, Andrea Lodi, Neil Yorke-Smith, Karen Aardal
Learning to branch with Tree MDPs. CoRR (2022)
Prateek Gupta, Elias Boutros Khalil, Didier Chételat, Maxime Gasse, Andrea Lodi, Yoshua Bengio, M. Pawan Kumar
Lookback for Learning to Branch. Trans. Mach. Learn. Res. 2022 (2022)
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 Yangm, 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, Chen Binbin, He Minggui, Hao Hao, Zhang Zhiyu, An Zhiwu, Mao Kun
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights. CoRR (2022)
Antoine Prouvost, Justin Dumouchelle, Maxime Gasse, Didier Chételat, Andrea Lodi
Ecole: A Library for Learning Inside MILP Solvers. CoRR (2021)
Maxime Gasse, Damien Grasset, Guillaume Gaudron, Pierre-Yves Oudeyer
Causal Reinforcement Learning using Observational and Interventional Data. CoRR (2021)
Léo Milecki, Jonathan Porée, Hatim Belgharbi, Chloé Bourquin, Rafat Damseh, Patrick Delafontaine-Martel, Frédéric Lesage, Maxime Gasse, Jean Provost
A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy. IEEE Trans. Medical Imaging 40 (5) (2021)
Maxime Gasse, Simon Bowly, Quentin Cappart, Jonas Charfreitag, Laurent Charlin, Didier Chételat, Antonia Chmiela, Justin Dumouchelle, Ambros M. Gleixner, Aleksandr M. Kazachkov, Elias 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
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights. NeurIPS (Competition and Demos) (2021)
Y. Cem Sübakan, Maxime Gasse, Laurent Charlin
On the Effectiveness of Two-Step Learning for Latent-Variable Models. MLSP (2020)
Antoine Prouvost, Justin Dumouchelle, Lara Scavuzzo, Maxime Gasse, Didier Chételat, Andrea Lodi
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers. CoRR (2020)
Prateek Gupta, Maxime Gasse, Elias B. Khalil, M. Pawan Kumar, Andrea Lodi, Yoshua Bengio
Hybrid Models for Learning to Branch. CoRR (2020)
Prateek Gupta, Maxime Gasse, Elias B. Khalil, Pawan Kumar Mudigonda, Andrea Lodi, Yoshua Bengio
Hybrid Models for Learning to Branch. NeurIPS (2020)
Benjamin Müller, Gonzalo Muñoz, Maxime Gasse, Ambros M. Gleixner, Andrea Lodi, Felipe Serrano
On Generalized Surrogate Duality in Mixed-Integer Nonlinear Programming. IPCO (2020)
Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi
Exact Combinatorial Optimization with Graph Convolutional Neural Networks. CoRR (2019)
Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi
Exact Combinatorial Optimization with Graph Convolutional Neural Networks. NeurIPS (2019)
Denis Lecoeuche, Alex Aussem, Maxime Gasse
On the use of binary stochastic autoencoders for multi-label classification under the zero-one loss. INNS Conference on Big Data (2018)
Maxime Gasse, Alex Aussem
F-Measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets. ECML/PKDD (1) (2016)
Maxime Gasse, Alex Aussem
Identifying the irreducible disjoint factors of a multivariate probability distribution. Probabilistic Graphical Models (2016)
Maxime Gasse, Alex Aussem
F-measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets. CoRR (2016)
Maxime Gasse, Alex Aussem, Haytham Elghazel
An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning. CoRR (2015)
Maxime Gasse, Alex Aussem, Haytham Elghazel
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning. CoRR (2015)
Maxime Gasse, Alexandre Aussem, Haytham Elghazel
On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property. ICML (2015)
Maxime Gasse, Alex Aussem, Haytham Elghazel
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning. Expert Syst. Appl. 41 (15) (2014)
Alex Aussem, Pascal Caillet, Zara Klemm, Maxime Gasse, Anne-Marie Schott, Michel Ducher
Analysis of risk factors of hip fracture with causal Bayesian networks. IWBBIO (2014)
Maxime Gasse, Alex Aussem, Haytham Elghazel
An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning. ECML/PKDD (1) (2012)

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Publication Activity (10 Years)

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Maxime Gasse