Philippe Rigollet

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Jonathan Niles-WeedNew York UniversityVerified email at cims.nyu.edu
Jan-Christian HütterGenentechVerified email at gene.com
Sinho ChewiYale UniversityVerified email at yale.edu
Thibaut Le GouicInstitut de Mathématiques de MarseilleVerified email at math.cnrs.fr
Geoffrey SchiebingerUniversity of British ColumbiaVerified email at math.ubc.ca
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
Chen LuMITVerified email at mit.edu
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
Jason M. AltschulerUPennVerified email at upenn.edu
Afonso S. BandeiraProfessor of Mathematics, ETH ZurichVerified email at math.ethz.ch
Paxton TurnerArbol Inc.Verified email at alum.mit.edu
George StepaniantsComputing and Mathematical Sciences, California Institute of TechnologyVerified email at caltech.edu
Xin TongAssociate Professor, Department of Data Sciences and Operations, University of Southern CaliforniaVerified email at marshall.usc.edu
Erik SnowbergDavid Eccles School of Business, University of UtahVerified email at eccles.utah.edu
Sylvain ChassangProfessor of Economics, Princeton UniversityVerified email at princeton.edu

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	904	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	705	2017
Complexity theoretic lower bounds for sparse principal component detection Q Berthet, P Rigollet Conference on learning theory, 1046-1066, 2013	388*	2013
Optimal detection of sparse principal components in high dimension Q Berthet, P Rigollet	355	2013
High-dimensional statistics P Rigollet, JC Hütter arXiv preprint arXiv:2310.19244, 2023	338*	2023
Exponential screening and optimal rates of sparse estimation P Rigollet, A Tsybakov The Annals of Statistics 39 (2), 731-771, 2011	280	2011
Batched bandit problems V Perchet, P Rigollet, S Chassang, E Snowberg	277	2016
Generalization error bounds in semi-supervised classification under the cluster assumption P Rigollet Arxiv preprint math/0604233, 2006	226	2006
The multi-armed bandit problem with covariates V Perchet, P Rigollet	215	2013
Learning by mirror averaging A Juditsky, P Rigollet, AB Tsybakov The Annals of Statistics 36 (5), 2183-2206, 2008	192	2008
Optimal rates for plug-in estimators of density level sets P Rigollet, R Vert Bernoulli 15 (4), 1154-1178, 2009	152	2009
Linear and convex aggregation of density estimators P Rigollet, AB Tsybakov Mathematical Methods of Statistics 16, 260-280, 2007	145	2007
Nonparametric bandits with covariates P Rigollet, A Zeevi arXiv preprint arXiv:1003.1630, 2010	142	2010
Sparse estimation by exponential weighting P Rigollet, A Tsybakov Statistical Science 27 (4), 558-575, 2012	141	2012
Minimax estimation of smooth optimal transport maps JC Hütter, P Rigollet	137	2021
Neyman-pearson classification, convexity and stochastic constraints P Rigollet, X Tong Journal of machine learning research, 2011	124	2011
Estimation of wasserstein distances in the spiked transport model J Niles-Weed, P Rigollet Bernoulli 28 (4), 2663-2688, 2022	121	2022
Bounded regret in stochastic multi-armed bandits S Bubeck, V Perchet, P Rigollet Conference on Learning Theory, 122-134, 2013	118	2013
Gradient descent algorithms for Bures-Wasserstein barycenters S Chewi, T Maunu, P Rigollet, AJ Stromme Conference on Learning Theory, 1276-1304, 2020	117	2020
Optimal rates for total variation denoising JC Hütter, P Rigollet Conference on Learning Theory, 1115-1146, 2016	113	2016

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