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2020 – today
- 2024
[c13]Alexandre Drouin, Maxime Gasse, Massimo Caccia, Issam H. Laradji, Manuel Del Verme, Tom Marty, David Vázquez, Nicolas Chapados, Alexandre Lacoste:
WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks? ICML 2024- [i15]Brice Rauby, Paul Xing, Jonathan Porée, Maxime Gasse, Jean Provost:
Pruning Sparse Tensor Neural Networks Enables Deep Learning for 3D Ultrasound Localization Microscopy. CoRR abs/2402.09359 (2024) - [i14]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 abs/2403.07718 (2024) - [i13]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 abs/2407.05291 (2024) - 2023
- [j5]Maxime Gasse, Damien Grasset, Guillaume Gaudron, Pierre-Yves Oudeyer:
Using Confounded Data in Latent Model-Based Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023) - [i12]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 abs/2310.08143 (2023) - 2022
- [j4]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): 89-118 (2022) - [j3]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) - [c12]Lara Scavuzzo, Feng Yang Chen, Didier Chételat, Maxime Gasse, Andrea Lodi, Neil Yorke-Smith, Karen I. Aardal:
Learning to Branch with Tree MDPs. NeurIPS 2022 - [i11]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:
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights. CoRR abs/2203.02433 (2022) - [i10]Lara Scavuzzo, Feng Yang Chen, Didier Chételat, Maxime Gasse, Andrea Lodi, Neil Yorke-Smith, Karen I. Aardal:
Learning to branch with Tree MDPs. CoRR abs/2205.11107 (2022) - [i9]Prateek Gupta, Elias B. Khalil, Didier Chételat, Maxime Gasse, Yoshua Bengio, Andrea Lodi, M. Pawan Kumar:
Lookback for Learning to Branch. CoRR abs/2206.14987 (2022) - 2021
- [j2]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): 1428-1437 (2021) - [c11]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:
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights. NeurIPS (Competition and Demos) 2021: 220-231 - [i8]Antoine Prouvost, Justin Dumouchelle, Maxime Gasse, Didier Chételat, Andrea Lodi:
Ecole: A Library for Learning Inside MILP Solvers. CoRR abs/2104.02828 (2021) - [i7]Maxime Gasse, Damien Grasset, Guillaume Gaudron, Pierre-Yves Oudeyer:
Causal Reinforcement Learning using Observational and Interventional Data. CoRR abs/2106.14421 (2021) - 2020
- [c10]Benjamin Müller, Gonzalo Muñoz, Maxime Gasse, Ambros M. Gleixner, Andrea Lodi, Felipe Serrano:
On Generalized Surrogate Duality in Mixed-Integer Nonlinear Programming. IPCO 2020: 322-337 - [c9]Y. Cem Sübakan, Maxime Gasse, Laurent Charlin:
On the Effectiveness of Two-Step Learning for Latent-Variable Models. MLSP 2020: 1-6 - [c8]Prateek Gupta, Maxime Gasse, Elias B. Khalil, Pawan Kumar Mudigonda, Andrea Lodi, Yoshua Bengio:
Hybrid Models for Learning to Branch. NeurIPS 2020 - [i6]Prateek Gupta, Maxime Gasse, Elias B. Khalil, M. Pawan Kumar, Andrea Lodi, Yoshua Bengio:
Hybrid Models for Learning to Branch. CoRR abs/2006.15212 (2020) - [i5]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 abs/2011.06069 (2020)
2010 – 2019
- 2019
- [c7]Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi:
Exact Combinatorial Optimization with Graph Convolutional Neural Networks. NeurIPS 2019: 15554-15566 - [i4]Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi:
Exact Combinatorial Optimization with Graph Convolutional Neural Networks. CoRR abs/1906.01629 (2019) - 2018
- [c6]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: 71-80 - 2017
- [b1]Maxime Gasse:
Probabilistic Graphical Model Structure Learning: Application to Multi-Label Classification. (Apprentissage de Structure de Modèles Graphiques Probabilistes: Application à la Classification Multi-Label). Claude Bernard University Lyon 1, 2017 - 2016
- [c5]Maxime Gasse, Alex Aussem:
Identifying the irreducible disjoint factors of a multivariate probability distribution. Probabilistic Graphical Models 2016: 183-194 - [c4]Maxime Gasse, Alex Aussem:
F-Measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets. ECML/PKDD (1) 2016: 619-631 - [i3]Maxime Gasse, Alex Aussem:
F-measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets. CoRR abs/1604.07759 (2016) - 2015
- [c3]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: 2531-2539 - [i2]Maxime Gasse, Alex Aussem, Haytham Elghazel:
An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning. CoRR abs/1505.05004 (2015) - [i1]Maxime Gasse, Alex Aussem, Haytham Elghazel:
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning. CoRR abs/1506.05692 (2015) - 2014
- [j1]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): 6755-6772 (2014) - [c2]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: 1074-1085 - 2012
- [c1]Maxime Gasse, Alex Aussem, Haytham Elghazel:
An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning. ECML/PKDD (1) 2012: 58-73
Coauthor Index
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