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Maxime Gasse
Maxime Gasse
ServiceNow Research
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Title
Cited by
Cited by
Year
Exact combinatorial optimization with graph convolutional neural networks
M Gasse, D Chételat, N Ferroni, L Charlin, A Lodi
Advances in neural information processing systems 32, 2019
5012019
The SCIP optimization suite 7.0
G Gamrath, D Anderson, K Bestuzheva, WK Chen, L Eifler, M Gasse, ...
3002020
Hybrid models for learning to branch
P Gupta, M Gasse, E Khalil, P Mudigonda, A Lodi, Y Bengio
Advances in neural information processing systems 33, 18087-18097, 2020
1292020
High-quality plane wave compounding using convolutional neural networks
M Gasse, F Millioz, E Roux, D Garcia, H Liebgott, D Friboulet
IEEE transactions on ultrasonics, ferroelectrics, and frequency control 64 …, 2017
1242017
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning
M Gasse, A Aussem, H Elghazel
Expert Systems with Applications 41 (15), 6755-6772, 2014
1052014
A deep learning framework for spatiotemporal ultrasound localization microscopy
L Milecki, J Porée, H Belgharbi, C Bourquin, R Damseh, ...
IEEE Transactions on Medical Imaging 40 (5), 1428-1437, 2021
642021
Ecole: A gym-like library for machine learning in combinatorial optimization solvers
A Prouvost, J Dumouchelle, L Scavuzzo, M Gasse, D Chételat, A Lodi
arXiv preprint arXiv:2011.06069, 2020
592020
Causal reinforcement learning using observational and interventional data
M Gasse, D Grasset, G Gaudron, PY Oudeyer
arXiv preprint arXiv:2106.14421, 2021
572021
Learning to branch with tree mdps
L Scavuzzo, F Chen, D Chételat, M Gasse, A Lodi, N Yorke-Smith, ...
Advances in neural information processing systems 35, 18514-18526, 2022
432022
An experimental comparison of hybrid algorithms for Bayesian network structure learning
M Gasse, A Aussem, H Elghazel
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2012
412012
The machine learning for combinatorial optimization competition (ml4co): Results and insights
M Gasse, S Bowly, Q Cappart, J Charfreitag, L Charlin, D Chételat, ...
NeurIPS 2021 competitions and demonstrations track, 220-231, 2022
232022
Lookback for learning to branch
P Gupta, EB Khalil, D Chetélat, M Gasse, Y Bengio, A Lodi, MP Kumar
arXiv preprint arXiv:2206.14987, 2022
222022
On the optimality of multi-label classification under subset zero-one loss for distributions satisfying the composition property
M Gasse, A Aussem, H Elghazel
International Conference on Machine Learning, 2531-2539, 2015
192015
On generalized surrogate duality in mixed-integer nonlinear programming
B Müller, G Muñoz, M Gasse, A Gleixner
9*2021
F-measure maximization in multi-label classification with conditionally independent label subsets
M Gasse, A Aussem
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2016
92016
On generalized surrogate duality in mixed-integer nonlinear programming
B Müller, G Muñoz, M Gasse, A Gleixner, A Lodi, F Serrano
International Conference on Integer Programming and Combinatorial …, 2020
82020
Ecole: A library for learning inside MILP solvers
A Prouvost, J Dumouchelle, M Gasse, D Chételat, A Lodi
arXiv preprint arXiv:2104.02828, 2021
62021
WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?
A Drouin, M Gasse, M Caccia, IH Laradji, M Del Verme, T Marty, ...
arXiv preprint arXiv:2403.07718, 2024
52024
Probabilistic graphical model structure learning: application to multi-label classification
M Gasse
Université de Lyon, 2017
52017
On the use of binary stochastic autoencoders for multi-label classification under the zero-one loss
D Lecoeuche, A Aussem, M Gasse
Procedia computer science 144, 71-80, 2018
42018
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Articles 1–20