Philippe Rigollet

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Jonathan Niles-WeedNew York UniversityVerified email at cims.nyu.edu
Jan-Christian HütterGenentechVerified email at gene.com
Thibaut Le GouicInstitut de Mathématiques de MarseilleVerified email at math.cnrs.fr
Sinho ChewiYale UniversityVerified email at yale.edu
Vianney PerchetCrest, ENSAE & Criteo AI LabVerified email at normalesup.org
Quentin BerthetGoogle DeepMind, ParisVerified email at google.com
Austin J. StrommeENSAE, CRESTVerified email at ensae.fr
Brian ClearyAssistant Professor, Boston UniversityVerified email at bu.edu
Cheng MaoGeorgia Institute of TechnologyVerified email at math.gatech.edu
Tyler MaunuBrandeis UniversityVerified email at brandeis.edu
Anatoli JuditskyProfessor at Université Grenoble-AlpesVerified email at univ-grenoble-alpes.fr
Afonso S. BandeiraProfessor of Mathematics, ETH ZurichVerified email at math.ethz.ch
Erik SnowbergDavid Eccles School of Business, University of UtahVerified email at eccles.utah.edu
Sylvain ChassangProfessor of Economics, Princeton UniversityVerified email at princeton.edu
Ankur MoitraNorbert Wiener Professor of Mathematics, MITVerified email at mit.edu
Victor-Emmanuel BrunelENSAE-CREST, Professor in StatisticsVerified email at ensae.fr
Xin TongAssociate Professor, Department of Data Sciences and Operations, University of Southern CaliforniaVerified email at marshall.usc.edu
Tong ZhangUIUCVerified email at tongzhang-ml.org
John C UrschelMITVerified email at mit.edu
Mor NitzanThe Hebrew University of JerusalemVerified email at mail.huji.ac.il

Philippe Rigollet

Massachusetts Institute of Technology

Verified email at math.mit.edu - Homepage

Statistics Machine Learning Optimal Transport


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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	827	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	670	2017
Complexity theoretic lower bounds for sparse principal component detection Q Berthet, P Rigollet Conference on learning theory, 1046-1066, 2013	381*	2013
Optimal detection of sparse principal components in high dimension Q Berthet, P Rigollet	346	2013
High-dimensional statistics P Rigollet, JC Hütter arXiv preprint arXiv:2310.19244, 2023	305*	2023
Exponential screening and optimal rates of sparse estimation P Rigollet, A Tsybakov The Annals of Statistics 39 (2), 731-771, 2011	277	2011
Batched bandit problems V Perchet, P Rigollet, S Chassang, E Snowberg	261	2016
Generalization error bounds in semi-supervised classification under the cluster assumption P Rigollet Arxiv preprint math/0604233, 2006	220	2006
The multi-armed bandit problem with covariates V Perchet, P Rigollet	208	2013
Learning by mirror averaging A Juditsky, P Rigollet, AB Tsybakov The Annals of Statistics 36 (5), 2183-2206, 2008	189	2008
Optimal rates for plug-in estimators of density level sets P Rigollet, R Vert Bernoulli 15 (4), 1154-1178, 2009	150	2009
Linear and convex aggregation of density estimators P Rigollet, AB Tsybakov Mathematical Methods of Statistics 16, 260-280, 2007	144	2007
Sparse estimation by exponential weighting P Rigollet, A Tsybakov Statistical Science 27 (4), 558-575, 2012	138	2012
Nonparametric bandits with covariates P Rigollet, A Zeevi arXiv preprint arXiv:1003.1630, 2010	137	2010
Minimax estimation of smooth optimal transport maps JC Hütter, P Rigollet	124	2021
Neyman-pearson classification, convexity and stochastic constraints P Rigollet, X Tong Journal of machine learning research, 2011	119	2011
Bounded regret in stochastic multi-armed bandits S Bubeck, V Perchet, P Rigollet Conference on Learning Theory, 122-134, 2013	116	2013
Estimation of wasserstein distances in the spiked transport model J Niles-Weed, P Rigollet Bernoulli 28 (4), 2663-2688, 2022	110	2022
Optimal rates for total variation denoising JC Hütter, P Rigollet Conference on Learning Theory, 1115-1146, 2016	108	2016
Kullback-Leibler aggregation and misspecified generalized linear models P Rigollet Arxiv preprint arXiv:0911.2919, 2009	104	2009

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