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20062022
most citedHadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication

97 citations · 543 across the 40 of their papers we have counts for

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9 papers · 1 filter

cs.IT2021

Principal Bit Analysis: Autoencoding with Schur-Concave Loss

Sourbh Bhadane, Aaron B. Wagner, Jayadev Acharya

We consider a linear autoencoder in which the latent variables are quantized, or corrupted by noise, and the constraint is Schur-concave in the set of latent variances. Although fi…

cs.IT2021★ 4 cited

Robust Testing and Estimation under Manipulation Attacks

Jayadev Acharya, Ziteng Sun, Huanyu Zhang

We study robust testing and estimation of discrete distributions in the strong contamination model. We consider both the "centralized setting" and the "distributed setting with inf…

cs.IT2020

Estimating Sparse Discrete Distributions Under Local Privacy and Communication Constraints

Jayadev Acharya, Peter Kairouz, Yuhan Liu +1

We consider the problem of estimating sparse discrete distributions under local differential privacy (LDP) and communication constraints. We characterize the sample complexity for…

cs.IT2019★ 1 cited

Estimating Entropy of Distributions in Constant Space

Jayadev Acharya, Sourbh Bhadane, Piotr Indyk +1

We consider the task of estimating the entropy of -ary distributions from samples in the streaming model, where space is limited. Our main contribution is an algorithm that requ…

cs.IT2019★ 27 cited

Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters

Jayadev Acharya, Ziteng Sun

We consider the problems of distribution estimation and heavy hitter (frequency) estimation under privacy and communication constraints. While these constraints have been studied s…

cs.IT2017

Improved Bounds for Universal One-Bit Compressive Sensing

Jayadev Acharya, Arnab Bhattacharyya, Pritish Kamath

Unlike compressive sensing where the measurement outputs are assumed to be real-valued and have infinite precision, in "one-bit compressive sensing", measurements are quantized to…