Learning To Branch with Tree MDPs.
Lara Scavuzzo and Feng Yang Chen and Maxime Gasse and Didier Chételat and Andrea Lodi and Neil Yorke-Smith and Karen Aardal
Neural Information Processing Systems (NeurIPS), 2022
Publications
Combinatorial Optimization
Machine Learning for Combinatorial Optimization.
Maxime Gasse and Andrea Lodi
Encyclopedia of Optimization, 2022
Lookback for Learning to Branch.
Prateek Gupta, Elias Khalil, Didier Chételat, Maxime Gasse, Yoshua Bengio, Andrea Lodi, Mudigonda Pawan Kumar
Transactions on Machine Learning Research (TMLR), 2022
On Generalized Surrogate Duality in Mixed-Integer Nonlinear Programming. [doi] [pdf]
Benjamin Müller, Gonzalo Muñoz, Maxime Gasse, Ambros Gleixner, Andrea Lodi and Felipe Serrano
Mathematical Programming, vol. 192 (1), pp. 89-118, 2022
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights. [pdf]
Maxime Gasse, Simon Bowly, Quentin Cappart, Jonas Charfreitag, Laurent Charlin, Didier Chételat, Antonia Chmiela, Justin Dumouchelle, Ambros Gleixner, Aleksandr M. Kazachkov, Elias Khalil, Pawel Lichocki, Andrea Lodi, Miles Lubin, Chris J. Maddison, Morris Christopher, 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, Shengcheng 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 and Mao Kun
Neural Information Processing Systems (NeurIPS) - Competitions and Demonstrations Track, 2021
Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers. [pdf]
Antoine Prouvost, Justin Dumouchelle, Lara Scavuzzo, Maxime Gasse, Didier Chételat, Andrea Lodi
Learning Meets Combinatorial Algorithms NeurIPS Workshop (LMCA @ NeurIPS), 2020
Hybrid Models for Learning to Branch. [pdf]
Prateek Gupta, Maxime Gasse, Elias Khalil, Mudigonda Pawan Kumar, Andrea Lodi, Yoshua Bengio
Neural Information Processing Systems (NeurIPS), 2020
On Generalized Surrogate Duality in Mixed-Integer Nonlinear Programming. [doi] [pdf]
Benjamin Müller, Gonzalo Muñoz, Maxime Gasse, Ambros Gleixner, Andrea Lodi and Felipe Serrano
21st Conference on Integer Programming and Combinatorial Optimization (IPCO), 2020
Exact Combinatorial Optimization with Graph Convolutional Neural Networks. [pdf]
Maxime Gasse, Didier Chételat, Nicola Ferroni, Laurent Charlin, Andrea Lodi
Neural Information Processing Systems (NeurIPS), 2019
Probabilistic Graphical Model
On the Effectiveness of Two-Step Learning for Latent-Variable Models. [doi] [pdf]
Cem Subakan, Maxime Gasse and Laurent Charlin
IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), 2020
Identifying the irreducible disjoint factors of a multivariate probability distribution. [pdf]
Maxime Gasse, Alex Aussem
International Conference on Probabilistic Graphical Models (PGM), pp. 183-194, 2016
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning. [doi] [pdf]
Maxime Gasse, Alex Aussem, Haytham Elghazel
Expert Systems With Applications (ESWA), vol. 41 (15), pp. 6755-6772, 2014
Analysis of risk factors of hip fracture with causal Bayesian networks. [pdf]
Alex Aussem, Pascal Caillet, Zara Klemm, Maxime Gasse, Anne-Marie Schott and Michel Ducher
International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO), pp. 1074-1085, 2014
An experimental comparison of hybrid algorithms for Bayesian network structure learning. [doi] [pdf]
Maxime Gasse, Alex Aussem, and Haytham Elghazel
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), pp. 58-73, 2012
Medical Imaging
A Deep Learning Framework for Spatiotemporal Ultrasound Localization Microscopy. [doi]
Léo Milecki, Jonathan Porée, Hatim Belgharbi, Chloé Bourquin, Rafat Damseh, Patrick Delafontaine-Martel, Frédéric Lesage, Maxime Gasse, Jean Provost
IEEE Transactions on Medical Imaging (TMI), vol. 40 (5), pp. 1428-1437, 2021
Accelerating plane wave imaging through deep learning-based reconstruction: An experimental study. [doi]
Maxime Gasse, Fabien Millioz, Emmanuel Roux, Damien Garcia, Hervé Liebgott, Denis Friboulet
IEEE International Ultrasonics Symposium (IUS), 2017
High-Quality Plane Wave Compounding Using Convolutional Neural Networks. [doi] [pdf]
Maxime Gasse, Fabien Millioz, Emmanuel Roux, Damien Garcia, Hervé Liebgott, Denis Friboulet
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (TUFFC), vol. 64 (10), pp. 1637-1639, 2017
Multi-label Classification
On the use of binary stochastic autoencoders for multi-label classification under the zero-one loss. [doi]
Denis Lecoeuche, Alex Aussem, Maxime Gasse
INNS Big Data and Deep Learning (INNS-BDDL), 2018
F-Measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets. [doi] [pdf]
Maxime Gasse, Alex Aussem
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), vol. 9851, pp. 619-631, 2016
On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property. [pdf]
Maxime Gasse, Alex Aussem, Haytham Elghazel
International Conference on Machine Learning (ICML), pp. 2531-2539, 2015
PhD thesis
Probabilistic Graphical Model Structure Learning: Application to Multi-Label Classification. [pdf] [slides]
Maxime Gasse
PhD Thesis, Claude Bernard University Lyon 1, 2017
Other
Optimal Sensor Locations for Polymer Injection Molding Process. [doi]
David Garcia, Ronan Le Goff, Maxime Gasse, Alex Aussem
European Scientific Association for Material Forming (ESAFORM), pp. 1724-1733, 2014