| Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming G Schiebinger, J Shu, M Tabaka, B Cleary, V Subramanian, A Solomon, ... Cell 176 (4), 928-943. e22, 2019 | 1140 | 2019 |
| Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration J Altschuler, J Niles-Weed, P Rigollet Advances in neural information processing systems 30, 2017 | 839 | 2017 |
| Complexity theoretic lower bounds for sparse principal component detection Q Berthet, P Rigollet Conference on learning theory, 1046-1066, 2013 | 430* | 2013 |
| High-dimensional statistics P Rigollet, JC Hütter arXiv preprint arXiv:2310.19244, 2023 | 406* | 2023 |
| Optimal detection of sparse principal components in high dimension Q Berthet, P Rigollet | 382 | 2013 |
| Batched bandit problems V Perchet, P Rigollet, S Chassang, E Snowberg | 304 | 2016 |
| Exponential screening and optimal rates of sparse estimation P Rigollet, A Tsybakov The Annals of Statistics 39 (2), 731-771, 2011 | 299 | 2011 |
| The multi-armed bandit problem with covariates V Perchet, P Rigollet | 241 | 2013 |
| Generalization error bounds in semi-supervised classification under the cluster assumption P Rigollet Arxiv preprint math/0604233, 2006 | 235 | 2006 |
| Learning by mirror averaging A Juditsky, P Rigollet, AB Tsybakov The Annals of Statistics 36 (5), 2183-2206, 2008 | 197 | 2008 |
| Minimax estimation of smooth optimal transport maps JC Hütter, P Rigollet | 175 | 2021 |
| Nonparametric bandits with covariates P Rigollet, A Zeevi arXiv preprint arXiv:1003.1630, 2010 | 168 | 2010 |
| Optimal rates for plug-in estimators of density level sets P Rigollet, R Vert Bernoulli 15 (4), 1154-1178, 2009 | 159 | 2009 |
| Linear and convex aggregation of density estimators P Rigollet, AB Tsybakov Mathematical Methods of Statistics 16 (3), 260-280, 2007 | 157 | 2007 |
| Sparse estimation by exponential weighting P Rigollet, A Tsybakov Statistical Science 27 (4), 558-575, 2012 | 152 | 2012 |
| Variational inference via Wasserstein gradient flows M Lambert, S Chewi, F Bach, S Bonnabel, P Rigollet Advances in Neural Information Processing Systems 35, 14434-14447, 2022 | 149 | 2022 |
| Estimation of Wasserstein distances in the spiked transport model J Niles-Weed, P Rigollet Bernoulli 28 (4), 2663-2688, 2022 | 149 | 2022 |
| A mathematical perspective on transformers B Geshkovski, C Letrouit, Y Polyanskiy, P Rigollet Bulletin of the American Mathematical Society 62 (3), 427-479, 2025 | 145 | 2025 |
| Gradient descent algorithms for Bures-Wasserstein barycenters S Chewi, T Maunu, P Rigollet, AJ Stromme Conference on Learning Theory, 1276-1304, 2020 | 144 | 2020 |
| Neyman-Pearson classification, convexity and stochastic constraints. P Rigollet, X Tong Journal of machine learning research 12 (10), 2011 | 141 | 2011 |
Jonathan Niles-WeedNew York UniversityVerified email at cims.nyu.edu
