V
Vladimir Braverman
Researcher at Johns Hopkins University
Publications - 185
Citations - 3374
Vladimir Braverman is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Computer science & Coreset. The author has an hindex of 25, co-authored 158 publications receiving 2475 citations. Previous affiliations of Vladimir Braverman include University of California, Los Angeles & Google.
Papers
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Proceedings Article
Streaming Coreset Constructions for M-Estimators.
TL;DR: This is the first streaming construction for any M -estimator that does not rely on the merge-and-reduce tree, and the coreset (Q, v) can be used in place of (P,w) for all operations concerning the COST function.
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Universal Streaming
TL;DR: It is shown that it is possible to collect universal statistics of polylogarithmic size, and it is proved that these universal statistics allow us after the fact to compute all other statistics that are computable with similar amounts of memory.
Book ChapterDOI
Symmetric Norm Estimation and Regression on Sliding Windows
TL;DR: In this paper, the authors study the problem of approximating symmetric norms (a norm on R n that is invariant under sign-flips and coordinate-wise permutations) in the sliding window model, where only the most recent updates define the underlying frequency vector.
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A Unified Approach for Clustering Problems on Sliding Windows
TL;DR: A data structure that extends smooth histograms as introduced by Braverman and Ostrovsky to operate on a broader class of functions is introduced, and it is shown that using only polylogarithmic space the authors can maintain a summary of the current window from which they can construct an O(1)-approximate clustering solution.
Posted Content
Linear and Sublinear Time Spectral Density Estimation.
TL;DR: In this paper, a simple and practical variant of the KPM algorithm can approximate the spectral density to Ω( √ log n) accuracy in the Wasserstein-1 distance with roughly O(1}/ √ n) matrix-vector multiplications with $A.