activity
20142017
most citedDifferentially Private Testing of Identity and Closeness of Discrete Distributions

21 citations · 48 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG201721 cited

Differentially Private Testing of Identity and Closeness of Discrete Distributions

Jayadev Acharya, Ziteng Sun, Huanyu Zhang

We study the fundamental problems of identity testing (goodness of fit), and closeness testing (two sample test) of distributions over elements, under differential privacy. Whi…

cs.IT20161 cited

A Unified Maximum Likelihood Approach for Optimal Distribution Property Estimation

Jayadev Acharya, Hirakendu Das, Alon Orlitsky +1

The advent of data science has spurred interest in estimating properties of distributions over large alphabets. Fundamental symmetric properties such as support size, support cover…

cs.LG201618 cited

Fast Algorithms for Segmented Regression

Jayadev Acharya, Ilias Diakonikolas, Jerry Li +1

We study the fixed design segmented regression problem: Given noisy samples from a piecewise linear function , we want to recover up to a desired accuracy in mean-squared er…

cs.DS2014

Testing Poisson Binomial Distributions

Jayadev Acharya, Constantinos Daskalakis

A Poisson Binomial distribution over variables is the distribution of the sum of independent Bernoullis. We provide a sample near-optimal algorithm for testing whether a di…

cs.IT20142 cited

Universal Compression of Envelope Classes: Tight Characterization via Poisson Sampling

Jayadev Acharya, Ashkan Jafarpour, Alon Orlitsky +1

The Poisson-sampling technique eliminates dependencies among symbol appearances in a random sequence. It has been used to simplify the analysis and strengthen the performance guara…

cs.DM20146 cited

String Reconstruction from Substring Compositions

Jayadev Acharya, Hirakendu Das, Olgica Milenkovic +2

Motivated by mass-spectrometry protein sequencing, we consider a simply-stated problem of reconstructing a string from the multiset of its substring compositions. We show that all…