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Philippe Rigollet
Researcher at Massachusetts Institute of Technology
Publications - 132
Citations - 5238
Philippe Rigollet is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Minimax & Estimator. The author has an hindex of 36, co-authored 123 publications receiving 4269 citations. Previous affiliations of Philippe Rigollet include Georgia Institute of Technology & University of Paris.
Papers
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Journal ArticleDOI
Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming.
Geoffrey Schiebinger,Geoffrey Schiebinger,Jian Shu,Jian Shu,Marcin Tabaka,Brian Cleary,Brian Cleary,Vidya Subramanian,Aryeh Solomon,Joshua Gould,Siyan Liu,Siyan Liu,Stacie Lin,Stacie Lin,Peter Berube,Lia Lee,Jenny Chen,Jenny Chen,Justin Brumbaugh,Philippe Rigollet,Konrad Hochedlinger,Rudolf Jaenisch,Aviv Regev,Aviv Regev,Aviv Regev,Eric S. Lander,Eric S. Lander,Eric S. Lander +27 more
TL;DR: Waddington-OT is introduced, an approach for studying developmental time courses to infer ancestor-descendant fates and model the regulatory programs that underlie them that sheds light on the process and outcome of reprogramming and provides a framework applicable to diverse temporal processes in biology.
Proceedings Article
Complexity Theoretic Lower Bounds for Sparse Principal Component Detection
TL;DR: The performance of a test is measured by the smallest signal strength that it can detect and a computationally efficient method based on semidefinite programming is proposed and it is proved that the statistical performance of this test cannot be strictly improved by any computationallyefficient method.
Proceedings Article
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
TL;DR: In this article, a new analysis of Sinkhorn iterations is presented, which also directly suggests a new greedy coordinate descent algorithm Greenkhorn with the same theoretical guarantees, which is shown to significantly outperform the classical sinkhorn algorithm in practice.
Journal ArticleDOI
Optimal detection of sparse principal components in high dimension
TL;DR: In this paper, a finite sample analysis of the detection levels for sparse principal components of a high-dimensional covariance matrix is performed, based on a sparse eigenvalue statistic.
Journal ArticleDOI
Optimal detection of sparse principal components in high dimension
TL;DR: In this article, a finite sample analysis of the detection levels for sparse principal components of a high-dimensional covariance matrix is performed, based on a sparse eigenvalue statistic.