activity
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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17 papers · 1 filter

cs.DS2022★ 3 cited

The Role of Interactivity in Structured Estimation

Jayadev Acharya, Clément L. Canonne, Ziteng Sun +1

We study high-dimensional sparse estimation under three natural constraints: communication constraints, local privacy constraints, and linear measurements (compressive sensing). Wi…

cs.DS2021★ 1 cited

Robust Estimation for Random Graphs

Jayadev Acharya, Ayush Jain, Gautam Kamath +2

We study the problem of robustly estimating the parameter of an Erdős-Rényi random graph on nodes, where a fraction of nodes may be adversarially corrupted. After showi…

cs.DS2021

Inference under Information Constraints III: Local Privacy Constraints

Jayadev Acharya, Clément L. Canonne, Cody Freitag +2

We study goodness-of-fit and independence testing of discrete distributions in a setting where samples are distributed across multiple users. The users wish to preserve the privacy…

cs.DS2020★ 8 cited

Unified lower bounds for interactive high-dimensional estimation under information constraints

Jayadev Acharya, Clément L. Canonne, Ziteng Sun +1

We consider distributed parameter estimation using interactive protocols subject to local information constraints such as bandwidth limitations, local differential privacy, and res…

cs.DS2020

Interactive Inference under Information Constraints

Jayadev Acharya, Clément L. Canonne, Yuhan Liu +2

We study the role of interactivity in distributed statistical inference under information constraints, e.g., communication constraints and local differential privacy. We focus on t…

cs.DS2019★ 2 cited

Domain Compression and its Application to Randomness-Optimal Distributed Goodness-of-Fit

Jayadev Acharya, Clément L. Canonne, Yanjun Han +2

We study goodness-of-fit of discrete distributions in the distributed setting, where samples are divided between multiple users who can only release a limited amount of information…