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Bharath K. Sriperumbudur
Researcher at Pennsylvania State University
Publications - 110
Citations - 6911
Bharath K. Sriperumbudur is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Reproducing kernel Hilbert space & Kernel (statistics). The author has an hindex of 32, co-authored 102 publications receiving 5720 citations. Previous affiliations of Bharath K. Sriperumbudur include University of Cambridge & University of California, San Diego.
Papers
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Journal ArticleDOI
Hilbert Space Embeddings and Metrics on Probability Measures
Bharath K. Sriperumbudur,Arthur Gretton,Kenji Fukumizu,Bernhard Schölkopf,Gert R. G. Lanckriet +4 more
TL;DR: It is shown that the distance between distributions under γk results from an interplay between the properties of the kernel and the distributions, by demonstrating that distributions are close in the embedding space when their differences occur at higher frequencies.
Proceedings Article
Optimal kernel choice for large-scale two-sample tests
Arthur Gretton,Dino Sejdinovic,Heiko Strathmann,Sivaraman Balakrishnan,Massimiliano Pontil,Kenji Fukumizu,Bharath K. Sriperumbudur +6 more
TL;DR: The new kernel selection approach yields a more powerful test than earlier kernel selection heuristics, and makes the kernel selection and test procedures suited to data streams, where the observations cannot all be stored in memory.
Proceedings Article
On the Convergence of the Concave-Convex Procedure
TL;DR: It is shown how Zangwill's global convergence theory of iterative algorithms provides a natural framework to prove the convergence of CCCP, allowing a more elegant and simple proof.
Journal ArticleDOI
Equivalence of distance-based and RKHS-based statistics in hypothesis testing
TL;DR: In this article, a unifying framework linking two classes of statistics used in two-sample and independence testing is presented, namely, the energy distance and distance covariances from the statistics literature; and the maximum mean discrepancy (MMD), that is, distances between embeddings of distributions to reproducing kernel Hilbert spaces.
Book
Kernel Mean Embedding of Distributions: A Review and Beyond
TL;DR: The kernel mean embedding (KME) as discussed by the authors is a generalization of the original feature map of support vector machines (SVMs) and other kernel methods, and it can be viewed as a generalisation of the SVM feature map.