1 citations · 1 across the 2 of their papers we have counts for
3 papers
Query-Efficient Locally Private Hypothesis Selection via the Scheffe Graph
Gautam Kamath, Alireza F. Pour, Matthew Regehr +1
We propose an algorithm with improved query-complexity for the problem of hypothesis selection under local differential privacy constraints. Given a set of probability distribu…
Differentially Private Post-Processing for Fair Regression
Ruicheng Xian, Qiaobo Li, Gautam Kamath +1
This paper describes a differentially private post-processing algorithm for learning fair regressors satisfying statistical parity, addressing privacy concerns of machine learning…
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition
Christian Janos Lebeda, Matthew Regehr, Gautam Kamath +1
We consider the problem of computing tight privacy guarantees for the composition of subsampled differentially private mechanisms. Recent algorithms can numerically compute the pri…