Philippe Rigollet

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Jonathan Niles-WeedNew York UniversityE-mail confirmado em cims.nyu.edu
Jan-Christian HütterGenentechE-mail confirmado em gene.com
Thibaut Le GouicInstitut de Mathématiques de MarseilleE-mail confirmado em math.cnrs.fr
Sinho ChewiYale UniversityE-mail confirmado em yale.edu
Vianney PerchetCrest, ENSAE & Criteo AI LabE-mail confirmado em normalesup.org
Quentin BerthetGoogle DeepMind, ParisE-mail confirmado em google.com
Austin J. StrommeENSAE, CRESTE-mail confirmado em ensae.fr
Brian ClearyAssistant Professor, Boston UniversityE-mail confirmado em bu.edu
Cheng MaoGeorgia Institute of TechnologyE-mail confirmado em math.gatech.edu
Tyler MaunuBrandeis UniversityE-mail confirmado em brandeis.edu
Anatoli JuditskyProfessor at Université Grenoble-AlpesE-mail confirmado em univ-grenoble-alpes.fr
Afonso S. BandeiraProfessor of Mathematics, ETH ZurichE-mail confirmado em math.ethz.ch
Erik SnowbergDavid Eccles School of Business, University of UtahE-mail confirmado em eccles.utah.edu
Sylvain ChassangProfessor of Economics, Princeton UniversityE-mail confirmado em princeton.edu
Ankur MoitraNorbert Wiener Professor of Mathematics, MITE-mail confirmado em mit.edu
Victor-Emmanuel BrunelENSAE-CREST, Professor in StatisticsE-mail confirmado em ensae.fr
Xin TongAssociate Professor, Department of Data Sciences and Operations, University of Southern CaliforniaE-mail confirmado em marshall.usc.edu
Tong ZhangUIUCE-mail confirmado em tongzhang-ml.org
John C UrschelMITE-mail confirmado em mit.edu
Mor NitzanThe Hebrew University of JerusalemE-mail confirmado em mail.huji.ac.il

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Philippe Rigollet

Massachusetts Institute of Technology

E-mail confirmado em math.mit.edu - Página inicial

Statistics Machine Learning Optimal Transport


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
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	816	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	665	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	344	2013
High-dimensional statistics P Rigollet, JC Hütter arXiv preprint arXiv:2310.19244, 2023	303*	2023
Exponential screening and optimal rates of sparse estimation P Rigollet, A Tsybakov The Annals of Statistics 39 (2), 731-771, 2011	276	2011
Batched bandit problems V Perchet, P Rigollet, S Chassang, E Snowberg	257	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	207	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	149	2009
Linear and convex aggregation of density estimators P Rigollet, AB Tsybakov Mathematical Methods of Statistics 16, 260-280, 2007	143	2007
Sparse estimation by exponential weighting P Rigollet, A Tsybakov Statistical Science 27 (4), 558-575, 2012	139	2012
Nonparametric bandits with covariates P Rigollet, A Zeevi arXiv preprint arXiv:1003.1630, 2010	136	2010
Minimax estimation of smooth optimal transport maps JC Hütter, P Rigollet	122	2021
Neyman-pearson classification, convexity and stochastic constraints P Rigollet, X Tong Journal of machine learning research, 2011	118	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	111	2022
Optimal rates for total variation denoising JC Hütter, P Rigollet Conference on Learning Theory, 1115-1146, 2016	105	2016
Kullback-Leibler aggregation and misspecified generalized linear models P Rigollet Arxiv preprint arXiv:0911.2919, 2009	104	2009

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