| Shallow and deep networks are near-optimal approximators of korobov functions M Blanchard, MA Bennouna International Conference on Learning Representations, 2021 | 17* | 2021 |
| Universal online learning: An optimistically universal learning rule M Blanchard Conference on Learning Theory, 1077-1125, 2022 | 14* | 2022 |
| Universal online learning with unbounded losses: Memory is all you need M Blanchard, R Cosson, S Hanneke International Conference on Algorithmic Learning Theory, 107-127, 2022 | 10 | 2022 |
| On the length of monotone paths in polyhedra M Blanchard, JA De Loera, Q Louveaux SIAM Journal on Discrete Mathematics 35 (3), 1746-1768, 2021 | 10 | 2021 |
| Universal regression with adversarial responses M Blanchard, P Jaillet The Annals of Statistics 51 (3), 1401-1426, 2023 | 8* | 2023 |
| Quadratic memory is necessary for optimal query complexity in convex optimization: Center-of-mass is pareto-optimal M Blanchard, J Zhang, P Jaillet The Thirty Sixth Conference on Learning Theory, 4696-4736, 2023 | 8 | 2023 |
| Universal online learning with bounded loss: Reduction to binary classification M Blanchard, R Cosson Conference on Learning Theory, 479-495, 2022 | 8 | 2022 |
| Probabilistic Bounds on the k-Traveling Salesman Problem and the Traveling Repairman Problem M Blanchard, A Jacquillat, P Jaillet Mathematics of Operations Research, 2023 | 5* | 2023 |
| Memory-constrained algorithms for convex optimization M Blanchard, J Zhang, P Jaillet Advances in Neural Information Processing Systems 36, 2024 | 4* | 2024 |
| Fréchet Mean Set Estimation in the Hausdorff Metric, via Relaxation M Blanchard, AQ Jaffe arXiv preprint arXiv:2212.12057, 2022 | 4 | 2022 |
| Contextual bandits and optimistically universal learning M Blanchard, S Hanneke, P Jaillet arXiv preprint arXiv:2301.00241, 2022 | 3 | 2022 |
| Tight Bounds for Local Glivenko-Cantelli M Blanchard, V Voracek International Conference on Algorithmic Learning Theory, 179-220, 2024 | 2 | 2024 |
| Gradient Descent is Pareto-Optimal in the Oracle Complexity and Memory Tradeoff for Feasibility Problems M Blanchard arXiv preprint arXiv:2404.06720, 2024 | 1 | 2024 |
| Adversarial Rewards in Universal Learning for Contextual Bandits M Blanchard, S Hanneke, P Jaillet arXiv preprint arXiv:2302.07186, 2023 | 1 | 2023 |
| Correlated Binomial Process M Blanchard, D Cohen, A Kontorovich arXiv preprint arXiv:2402.07058, 2024 | | 2024 |
| Fundamental Limits of Learning for Generalizability, Data Resilience, and Resource Efficiency M Blanchard Massachusetts Institute of Technology, 2024 | | 2024 |
Patrick JailletDugald C. Jackson Professor, EECS, MIT在 mit.edu 的电子邮件经过验证
