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20182026
most citedA Framework for Adversarially Robust Streaming Algorithms

23 citations · 85 across the 22 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

cs.DS2020

When is Approximate Counting for Conjunctive Queries Tractable?

Marcelo Arenas, Luis Alberto Croquevielle, Rajesh Jayaram +1

Conjunctive queries are one of the most common class of queries used in database systems, and the best studied in the literature. A seminal result of Grohe, Schwentick, and Segoufi…

cs.DS2020

Testing Positive Semi-Definiteness via Random Submatrices

Ainesh Bakshi, Nadiia Chepurko, Rajesh Jayaram

We study the problem of testing whether a matrix with bounded entries () is positive semi-definite (PSD), or…

cs.DS2020★ 6 cited

Learning and Testing Junta Distributions with Subcube Conditioning

Xi Chen, Rajesh Jayaram, Amit Levi +1

We study the problems of learning and testing junta distributions on with respect to the uniform distribution, where a distribution is a -junta if its probabili…

cs.DS2020★ 23 cited

A Framework for Adversarially Robust Streaming Algorithms

Omri Ben-Eliezer, Rajesh Jayaram, David P. Woodruff +1

We investigate the adversarial robustness of streaming algorithms. In this context, an algorithm is considered robust if its performance guarantees hold even if the stream is chose…

cs.DS2020

Span Recovery for Deep Neural Networks with Applications to Input Obfuscation

Rajesh Jayaram, David P. Woodruff, Qiuyi Zhang

The tremendous success of deep neural networks has motivated the need to better understand the fundamental properties of these networks, but many of the theoretical results propose…