| Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration J Altschuler, J Weed, P Rigollet Advances in Neural Information Processing Systems, 2017 | 670 | 2017 |
| Early-learning regularization prevents memorization of noisy labels S Liu, J Niles-Weed, N Razavian, C Fernandez-Granda Advances in neural information processing systems 33, 20331-20342, 2020 | 580 | 2020 |
| Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance J Weed, F Bach Bernoulli 25 (4A), 2620-2648, 2019 | 459 | 2019 |
| Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem G Mena, J Niles-Weed Advances in neural information processing systems 32, 2019 | 186 | 2019 |
| Massively scalable Sinkhorn distances via the Nyström method J Altschuler, F Bach, A Rudi, J Weed Advances in Neural Information Processing Systems, 2018 | 134* | 2018 |
| Estimation of wasserstein distances in the spiked transport model J Niles-Weed, P Rigollet Bernoulli 28 (4), 2663-2688, 2022 | 110 | 2022 |
| Minimax estimation of smooth densities in Wasserstein distance J Niles-Weed, Q Berthet The Annals of Statistics 50 (3), 1519-1540, 2022 | 103* | 2022 |
| Online learning in repeated auctions J Weed, V Perchet, P Rigollet Conference on Learning Theory, 1562-1583, 2016 | 102 | 2016 |
| Entropic estimation of optimal transport maps AA Pooladian, J Niles-Weed arXiv preprint arXiv:2109.12004, 2021 | 101 | 2021 |
| Entropic optimal transport is maximum-likelihood deconvolution P Rigollet, J Weed Comptes Rendus. Mathématique 356 (11-12), 1228-1235, 2018 | 94 | 2018 |
| Estimation under group actions: recovering orbits from invariants AS Bandeira, B Blum-Smith, J Kileel, J Niles-Weed, A Perry, AS Wein Applied and Computational Harmonic Analysis 66, 236-319, 2023 | 93 | 2023 |
| Plugin estimation of smooth optimal transport maps T Manole, S Balakrishnan, J Niles-Weed, L Wasserman arXiv preprint arXiv:2107.12364, 2021 | 90 | 2021 |
| The sample complexity of multireference alignment A Perry, J Weed, AS Bandeira, P Rigollet, A Singer SIAM Journal on Mathematics of Data Science 1 (3), 497-517, 2019 | 89 | 2019 |
| Uncoupled isotonic regression via minimum Wasserstein deconvolution P Rigollet, J Weed Information and Inference: A Journal of the IMA 8 (4), 691-717, 2019 | 79 | 2019 |
| Statistical optimal transport via factored couplings A Forrow, JC Hütter, M Nitzan, P Rigollet, G Schiebinger, J Weed The 22nd International Conference on Artificial Intelligence and Statistics …, 2018 | 78 | 2018 |
| Convergence of smoothed empirical measures with applications to entropy estimation Z Goldfeld, K Greenewald, J Niles-Weed, Y Polyanskiy IEEE Transactions on Information Theory 66 (7), 4368-4391, 2020 | 77 | 2020 |
| Optimal rates of estimation for multi-reference alignment AS Bandeira, P Rigollet, J Weed Mathematical Statistics and Learning 2 (1), 25-75, 2017 | 65 | 2017 |
| An explicit analysis of the entropic penalty in linear programming J Weed Conference on Learning Theory, 2018 | 54 | 2018 |
| Minimax rates and efficient algorithms for noisy sorting C Mao, J Weed, P Rigollet Algorithmic Learning Theory 83, 821–847, 2017 | 49 | 2017 |
| Diachronic modeling of the population within the medieval Greater Angkor Region settlement complex S Klassen, AK Carter, DH Evans, S Ortman, MT Stark, AA Loyless, ... Science Advances 7 (19), eabf8441, 2021 | 40 | 2021 |
Philippe RigolletMassachusetts Institute of TechnologyE-mail confirmado em math.mit.edu
