96 citations · 578 across the 58 of their papers we have counts for
7 papers · 1 filter
Private Data Stream Analysis for Universal Symmetric Norm Estimation
Vladimir Braverman, Joel Manning, Zhiwei Steven Wu +1
We study how to release summary statistics on a data stream subject to the constraint of differential privacy. In particular, we focus on releasing the family of symmetric norms, w…
Locally Private Hypothesis Selection
Sivakanth Gopi, Gautam Kamath, Janardhan Kulkarni +3
We initiate the study of hypothesis selection under local differential privacy. Given samples from an unknown probability distribution and a set of probability distribution…
Privately Learning Markov Random Fields
Huanyu Zhang, Gautam Kamath, Janardhan Kulkarni +1
We consider the problem of learning Markov Random Fields (including the prototypical example, the Ising model) under the constraint of differential privacy. Our learning goals incl…
Private Hypothesis Selection
Mark Bun, Gautam Kamath, Thomas Steinke +1
We provide a differentially private algorithm for hypothesis selection. Given samples from an unknown probability distribution and a set of probability distributions $\math…
Multidimensional Dynamic Pricing for Welfare Maximization
Aaron Roth, Aleksandrs Slivkins, Jonathan Ullman +1
We study the problem of a seller dynamically pricing distinct types of indivisible goods, when faced with the online arrival of unit-demand buyers drawn independently from an u…
Predicting with Distributions
Michael Kearns, Zhiwei Steven Wu
We consider a new learning model in which a joint distribution over vector pairs is determined by an unknown function that maps input vectors not to individual o…