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 ArticleDOI
One Sketch to Rule Them All: Rethinking Network Flow Monitoring with UnivMon
TL;DR: UnivMon is presented, a framework for flow monitoring which leverages recent theoretical advances and demonstrates that it is possible to achieve both generality and high accuracy, and evaluated using a range of trace-driven evaluations to show that it offers comparable (and sometimes better) accuracy relative to custom sketching solutions.
Proceedings Article
FetchSGD: Communication-Efficient Federated Learning with Sketching.
Daniel Rothchild,Ashwinee Panda,Enayat Ullah,Nikita Ivkin,Ion Stoica,Vladimir Braverman,Joseph E. Gonzalez,Raman Arora +7 more
TL;DR: This paper introduces a novel algorithm, called FetchSGD, which compresses model updates using a Count Sketch, and then takes advantage of the mergeability of sketches to combine model updates from many workers.
Proceedings ArticleDOI
Nitrosketch: robust and general sketch-based monitoring in software switches
Zaoxing Liu,Ran Ben-Basat,Gil Einziger,Yaron Kassner,Vladimir Braverman,Roy Friedman,Vyas Sekar +6 more
TL;DR: The design and implementation of NitroSketch is presented, a sketching framework that systematically addresses the performance bottlenecks of sketches without sacrificing robustness and generality and is implemented on three popular software platforms.
Posted Content
New Frameworks for Offline and Streaming Coreset Constructions
TL;DR: This work introduces a new technique for converting an offline coreset construction to the streaming setting, and provides the first generalizations of such coresets for handling outliers.
Proceedings ArticleDOI
Smooth Histograms for Sliding Windows
TL;DR: This paper presents a new smooth histograms method that improves the approximation error rate obtained via exponential histograms and provides the first approximation algorithms for the following functions: Lp norms for p notin, frequency moments, length of increasing subsequence and geometric mean.