An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning. - Maxime Gasse; Alex Aussem; Haytham Elghazel (ECML/PKDD (1), 2012)

Analysis of risk factors of hip fracture with causal Bayesian networks. - Alex Aussem; Pascal Caillet; Zara Klemm; Maxime Gasse; Anne-Marie Schott; Michel Ducher (IWBBIO, 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)

On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property. - Maxime Gasse; Alexandre Aussem; Haytham Elghazel (ICML, 2015)

Identifying the irreducible disjoint factors of a multivariate probability distribution. - Maxime Gasse; Alex Aussem (Probabilistic Graphical Models, 2016)

F-Measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets. - Maxime Gasse; Alex Aussem (ECML/PKDD (1), 2016)

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 (2017)

On the use of binary stochastic autoencoders for multi-label classification under the zero-one loss. - Denis Lecoeuche; Alex Aussem; Maxime Gasse (INNS Conference on Big Data, 2018)

Exact Combinatorial Optimization with Graph Convolutional Neural Networks. - Maxime Gasse; Didier Chételat; Nicola Ferroni; Laurent Charlin; Andrea Lodi 001 (NeurIPS, 2019)

On the Effectiveness of Two-Step Learning for Latent-Variable Models. - Y. Cem Sübakan; Maxime Gasse; Laurent Charlin (MLSP, 2020)

Hybrid Models for Learning to Branch. - Prateek Gupta; Maxime Gasse; Elias B. Khalil; Pawan Kumar Mudigonda; Andrea Lodi 001; Yoshua Bengio (NeurIPS, 2020)

Hybrid Models for Learning to Branch. - Prateek Gupta; Maxime Gasse; Elias B. Khalil; M. Pawan Kumar; Andrea Lodi 001; Yoshua Bengio (CoRR, 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 001 (CoRR, 2020)

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; et al. (NeurIPS (Competition and Demos), 2021)

Ecole: A Library for Learning Inside MILP Solvers. - Antoine Prouvost; Justin Dumouchelle; Maxime Gasse; Didier Chételat; Andrea Lodi 001 (CoRR, 2021)

Causal Reinforcement Learning using Observational and Interventional Data. - Maxime Gasse; Damien Grasset; Guillaume Gaudron; Pierre-Yves Oudeyer (CoRR, 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)

Learning to Branch with Tree MDPs. - Lara Scavuzzo; Feng Yang Chen; Didier Chételat; Maxime Gasse; Andrea Lodi 001; Neil Yorke-Smith; Karen I. Aardal (NeurIPS, 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 001; et al. (CoRR, 2022)

On generalized surrogate duality in mixed-integer nonlinear programming. - Benjamin Müller 002; Gonzalo Muñoz 001; Maxime Gasse; Ambros M. Gleixner; Andrea Lodi 001; Felipe Serrano 001 (Math. Program., 2022)

Lookback for Learning to Branch. - Prateek Gupta; Elias Boutros Khalil; Didier Chételat; Maxime Gasse; Andrea Lodi 001; Yoshua Bengio; M. Pawan Kumar (Trans. Mach. Learn. Res., 2022)

Using Confounded Data in Latent Model-Based Reinforcement Learning. - Maxime Gasse; Damien Grasset; Guillaume Gaudron; Pierre-Yves Oudeyer (Trans. Mach. Learn. Res., 2023)

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 001; Nicolas Chapados; Alexandre Lacoste (ICML, 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 (NeurIPS, 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 001; Nicolas Chapados; Alexandre Lacoste (CoRR, 2024)

Too Big to Fool: Resisting Deception in Language Models. - Mohammad Reza Samsami; Mats Leon Richter; Juan A. Rodríguez; Megh Thakkar; Sarath Chandar; Maxime Gasse (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 (IEEE Trans. Image Process., 2025)

The BrowserGym Ecosystem for Web Agent Research. - 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; et al. (Trans. Mach. Learn. Res., 2025)