| 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 | 464 | 2019 |
| The SCIP optimization suite 7.0 G Gamrath, D Anderson, K Bestuzheva, WK Chen, L Eifler, M Gasse, ... | 289 | 2020 |
| 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 | 124 | 2020 |
| 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 | 118 | 2017 |
| 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 | 97 | 2014 |
| 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 | 55 | 2021 |
| 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 | 54 | 2020 |
| Causal reinforcement learning using observational and interventional data M Gasse, D Grasset, G Gaudron, PY Oudeyer arXiv preprint arXiv:2106.14421, 2021 | 47 | 2021 |
| 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 | 41 | 2012 |
| 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 | 34 | 2022 |
| 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 | 20 | 2022 |
| 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 | 19 | 2022 |
| 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 | 19 | 2015 |
| 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 | 9 | 2016 |
| 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 | 8 | 2020 |
| Ecole: A library for learning inside MILP solvers A Prouvost, J Dumouchelle, M Gasse, D Chételat, A Lodi arXiv preprint arXiv:2104.02828, 2021 | 6 | 2021 |
| Probabilistic graphical model structure learning: application to multi-label classification M Gasse Université de Lyon, 2017 | 5 | 2017 |
| 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 | 4 | 2018 |
| Accelerating plane wave imaging through deep learning-based reconstruction: An experimental study M Gasse, F Millioz, E Roux, H Liebgott, D Friboulet 2017 IEEE International Ultrasonic Symposium (IUS), 2017 | 3 | 2017 |
Andrea LodiCornell Tech and Technion -- IITVerified email at cornell.edu
