| Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration J Altschuler, J Weed, P Rigollet Advances in Neural Information Processing Systems, 2017 | 649 | 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 | 534 | 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 | 447 | 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 | 182 | 2019 |
| Massively scalable Sinkhorn distances via the Nyström method J Altschuler, F Bach, A Rudi, J Weed Advances in Neural Information Processing Systems, 2018 | 129* | 2018 |
| Estimation of wasserstein distances in the spiked transport model J Niles-Weed, P Rigollet Bernoulli 28 (4), 2663-2688, 2022 | 108 | 2022 |
| Minimax estimation of smooth densities in Wasserstein distance J Niles-Weed, Q Berthet The Annals of Statistics 50 (3), 1519-1540, 2022 | 100* | 2022 |
| Online learning in repeated auctions J Weed, V Perchet, P Rigollet Conference on Learning Theory, 1562-1583, 2016 | 100 | 2016 |
| Entropic optimal transport is maximum-likelihood deconvolution P Rigollet, J Weed Comptes Rendus. Mathématique 356 (11-12), 1228-1235, 2018 | 94 | 2018 |
| Entropic estimation of optimal transport maps AA Pooladian, J Niles-Weed arXiv preprint arXiv:2109.12004, 2021 | 93 | 2021 |
| 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 | 89 | 2023 |
| 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 | 85 | 2019 |
| Plugin estimation of smooth optimal transport maps T Manole, S Balakrishnan, J Niles-Weed, L Wasserman arXiv preprint arXiv:2107.12364, 2021 | 81 | 2021 |
| Uncoupled isotonic regression via minimum Wasserstein deconvolution P Rigollet, J Weed Information and Inference: A Journal of the IMA 8 (4), 691-717, 2019 | 78 | 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 | 77 | 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 | 75 | 2020 |
| Optimal rates of estimation for multi-reference alignment AS Bandeira, P Rigollet, J Weed Mathematical Statistics and Learning 2 (1), 25-75, 2017 | 64 | 2017 |
| An explicit analysis of the entropic penalty in linear programming J Weed Conference on Learning Theory, 2018 | 52 | 2018 |
| Minimax rates and efficient algorithms for noisy sorting C Mao, J Weed, P Rigollet Algorithmic Learning Theory 83, 821–847, 2017 | 46 | 2017 |
| Dimension-free log-Sobolev inequalities for mixture distributions HB Chen, S Chewi, J Niles-Weed Journal of Functional Analysis 281 (11), 109236, 2021 | 38 | 2021 |
