| Complexity theoretic lower bounds for sparse principal component detection Q Berthet, P Rigollet Conference on learning theory, COLT 2013, 1046-1066, 2013 | 381* | 2013 |
| Optimal detection of sparse principal components in high dimension Q Berthet, P Rigollet The Annals of Statistics 41 (4), 1780-1815, 2013 | 346 | 2013 |
| Efficient and modular implicit differentiation M Blondel, Q Berthet, M Cuturi, R Frostig, S Hoyer, F Llinares-López, ... Advances in Neural Information Processing Systems, NeurIPS 2022, 2021 | 232 | 2021 |
| Unsupervised alignment of embeddings with Wasserstein Procrustes E Grave, A Joulin, Q Berthet International Conference on Artificial Intelligence and Statistics, AISTATS 2019, 2018 | 230 | 2018 |
| Learning with differentiable perturbed optimizers Q Berthet, M Blondel, O Teboul, M Cuturi, JP Vert, F Bach Advances in Neural Information Processing Systems, NeurIPS 2020, 2020 | 226 | 2020 |
| Fast differentiable sorting and ranking M Blondel, O Teboul, Q Berthet, J Djolonga International Conference on Machine Learning, ICML 2020, 950-959, 2020 | 223 | 2020 |
| Statistical and computational trade-offs in estimation of sparse principal components T Wang, Q Berthet, RJ Samworth The Annals of Statistics 44 (5), 1896-1930, 2016 | 170 | 2016 |
| DeepConsensus improves the accuracy of sequences with a gap-aware sequence transformer G Baid, DE Cook, K Shafin, T Yun, F Llinares-López, Q Berthet, ... Nature Biotechnology 41 (2), 232-238, 2023 | 95* | 2023 |
| Estimation of smooth densities in Wasserstein distance J Weed, Q Berthet Proceedings of the Thirty-Second Conference on Learning Theory, COLT 2019, 2019 | 76 | 2019 |
| Exact recovery in the Ising blockmodel Q Berthet, P Rigollet, P Srivastava The Annals of Statistics 47 (4), 1805 - 1834, 2016 | 55 | 2016 |
| Average-case hardness of RIP certification T Wang, Q Berthet, Y Plan Advances in Neural Information Processing Systems, NeurIPS 2016, 2016 | 50 | 2016 |
| Deep embedding and alignment of protein sequences F Llinares-López, Q Berthet, M Blondel, O Teboul, JP Vert Nature Methods 20 (1), 104-111, 2023 | 41 | 2023 |
| Fast rates for bandit optimization with Upper-Confidence Frank-Wolfe Q Berthet, V Perchet Advances in Neural Information Processing Systems, NeurIPS 2017, 2017 | 39 | 2017 |
| Minimax estimation of smooth densities in Wasserstein distance J Niles-Weed, Q Berthet The Annals of Statistics 50 (3), 1519-1540, 2022 | 30 | 2022 |
| Statistical and computational rates in graph logistic regression Q Berthet, N Baldin International Conference on Artificial Intelligence and Statistics, AISTATS …, 2020 | 28* | 2020 |
| Self-supervised learning of audio representations from permutations with differentiable ranking AN Carr, Q Berthet, M Blondel, O Teboul, N Zeghidour IEEE Signal Processing Letters 28, 708-712, 2021 | 25 | 2021 |
| Noisy adaptive group testing using Bayesian sequential experimental design M Cuturi, O Teboul, Q Berthet, A Doucet, JP Vert arXiv preprint arXiv:2004.12508, 2020 | 23 | 2020 |
| Stochastic optimization for regularized Wasserstein estimators M Ballu, Q Berthet, F Bach International Conference on Machine Learning, ICML 2020, 602-612, 2020 | 22 | 2020 |
| Regression as classification: Influence of task formulation on neural network features L Stewart, F Bach, Q Berthet, JP Vert International Conference on Artificial Intelligence and Statistics, 11563-11582, 2023 | 18 | 2023 |
| Resource allocation for statistical estimation Q Berthet, V Chandrasekaran Proceedings of the IEEE 104 (1), 111-125, 2015 | 15 | 2015 |
Mathieu BlondelGoogleVerified email at google.com
