| 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 | 532 | 2019 |
| Combinatorial optimization and reasoning with graph neural networks Q Cappart, D Chételat, EB Khalil, A Lodi, C Morris, P Velickovic J. Mach. Learn. Res. 24, 130:1-130:61, 2023 | 384 | 2023 |
| 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 | 62 | 2020 |
| Learning to Branch with Tree MDPs L Scavuzzo, FY Chen, D Chételat, M Gasse, A Lodi, N Yorke-Smith, ... Advances in Neural Information Processing Systems 35, 2022 | 47 | 2022 |
| Improved multivariate normal mean estimation with unknown covariance when is greater than D Chételat, MT Wells | 29 | 2012 |
| Learning to Compare Nodes in Branch and Bound with Graph Neural Networks AG Labassi, D Chételat, A Lodi Advances in Neural Information Processing Systems 35, 2022 | 27 | 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 | 25 | 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 | 24 | 2022 |
| Optimal two-step prediction in regression D Chételat, J Lederer, J Salmon | 22 | 2017 |
| The middle-scale asymptotics of Wishart matrices D Chételat, MT Wells | 12 | 2019 |
| Improved second order estimation in the singular multivariate normal model D Chételat, MT Wells Journal of Multivariate Analysis 147, 1-19, 2016 | 11 | 2016 |
| Exploring the power of graph neural networks in solving linear optimization problems C Qian, D Chételat, C Morris International Conference on Artificial Intelligence and Statistics, 1432-1440, 2024 | 10 | 2024 |
| 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 |
| Combinatorial optimization and reasoning with graph neural networks, 2021 Q Cappart, D Chételat, E Khalil, A Lodi, C Morris, P Veličković arXiv preprint arXiv:2102.09544, 0 | 6 | |
| Continuous cutting plane algorithms in integer programming D Chételat, A Lodi Operations Research Letters 51 (4), 439-445, 2023 | 4 | 2023 |
| Noise estimation in the spiked covariance model D Chételat, MT Wells arXiv preprint arXiv:1408.6440, 2014 | 4 | 2014 |
| Deep unsupervised anomaly detection in high-frequency markets C Poutré, D Chételat, M Morales The Journal of Finance and Data Science 10, 100129, 2024 | 1 | 2024 |
| GraSS: Combining Graph Neural Networks with Expert Knowledge for SAT Solver Selection Z Zhang, D Chételat, J Cotnareanu, A Ghose, W Xiao, HL Zhen, Y Zhang, ... Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and …, 2024 | | 2024 |
| Erratum: Improved multivariate normal mean estimation with unknown covariance when p is greater than n D Chételat The Annals of Statistics 52 (1), 412-412, 2024 | | 2024 |
| Learning to Predict Security Constraints for Large-Scale Unit Commitment Problems R Sterzinger, J Poland, MB Paulus, D Chételat 2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE), 1-5, 2023 | | 2023 |
