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Rajesh Jayaram

29 papers hereh-index 18829 citations42 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author12
  • middle author15
  • last author2

Across the 29 of 29 papers where every author was matched, so the position is known.

fields
  • cs.DS24
  • cs.LG3
  • cs.CL1
  • cs.DB1
same name
  • Rajesh Jayaram — 6 papers, h 3
  • Rajesh Jayaram — 5 papers, h 4
  • Rajesh Jayaram — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182026
most citedA Framework for Adversarially Robust Streaming Algorithms

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

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.DS2019★ 17 cited

Optimal Sketching for Kronecker Product Regression and Low Rank Approximation

Huaian Diao, Rajesh Jayaram, Zhao Song +2

We study the Kronecker product regression problem, in which the design matrix is a Kronecker product of two or more matrices. Given Ai​∈Rni​×di​ for $i=1,2…

cs.DS2019

Towards Optimal Moment Estimation in Streaming and Distributed Models

Rajesh Jayaram, David P. Woodruff

One of the oldest problems in the data stream model is to approximate the p-th moment ∥X∥pp​=∑i=1n​∣Xi​∣p of an underlying vector $\mathcal{X}…

cs.DS2019

$\text{#NFA}$ admits an FPRAS: Efficient Enumeration, Counting, and Uniform Generation for Logspace Classes

Marcelo Arenas, Luis Alberto Croquevielle, Rajesh Jayaram +1

In this work, we study two simple yet general complexity classes, based on logspace Turing machines, which provide a unifying framework for efficient query evaluation in areas like…

cs.DS2019

Weighted Reservoir Sampling from Distributed Streams

Rajesh Jayaram, Gokarna Sharma, Srikanta Tirthapura +1

We consider message-efficient continuous random sampling from a distributed stream, where the probability of inclusion of an item in the sample is proportional to a weight associat…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.