activity
20122023
most citedThe Discrete Gaussian for Differential Privacy

36 citations · 176 across the 38 of their papers we have counts for

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Showing 2019Show all

6 papers · 1 filter

cs.DS2019

Random Restrictions of High-Dimensional Distributions and Uniformity Testing with Subcube Conditioning

Clément L. Canonne, Xi Chen, Gautam Kamath +2

We give a nearly-optimal algorithm for testing uniformity of distributions supported on , which makes queries to a subcube condition…

cs.DS2019★ 4 cited

Finding monotone patterns in sublinear time

Omri Ben-Eliezer, Clément L. Canonne, Shoham Letzter +1

We study the problem of finding monotone subsequences in an array from the viewpoint of sublinear algorithms. For fixed and , we show that the n…

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…

cs.DS2019

Learning from satisfying assignments under continuous distributions

Clément L. Canonne, Anindya De, Rocco A. Servedio

What kinds of functions are learnable from their satisfying assignments? Motivated by this simple question, we extend the framework of De, Diakonikolas, and Servedio [DDS15], which…

cs.DS2019★ 9 cited

Private Identity Testing for High-Dimensional Distributions

Clément L. Canonne, Gautam Kamath, Audra McMillan +2

In this work we present novel differentially private identity (goodness-of-fit) testers for natural and widely studied classes of multivariate product distributions: Gaussians in $…

cs.DS2019

Inference under Information Constraints II: Communication Constraints and Shared Randomness

Jayadev Acharya, Clément L. Canonne, Himanshu Tyagi

A central server needs to perform statistical inference based on samples that are distributed over multiple users who can each send a message of limited length to the center. We st…