142 citations · 842 across the 49 of their papers we have counts for
6 papers · 1 filter
The Structure of Optimal Private Tests for Simple Hypotheses
Clément L. Canonne, Gautam Kamath, Audra McMillan +2
Hypothesis testing plays a central role in statistical inference, and is used in many settings where privacy concerns are paramount. This work answers a basic question about privat…
Anaconda: A Non-Adaptive Conditional Sampling Algorithm for Distribution Testing
Gautam Kamath, Christos Tzamos
We investigate distribution testing with access to non-adaptive conditional samples. In the conditional sampling model, the algorithm is given the following access to a distributio…
Privately Learning High-Dimensional Distributions
Gautam Kamath, Jerry Li, Vikrant Singhal +1
We present novel, computationally efficient, and differentially private algorithms for two fundamental high-dimensional learning problems: learning a multivariate Gaussian and lear…
Sever: A Robust Meta-Algorithm for Stochastic Optimization
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane +3
In high dimensions, most machine learning methods are brittle to even a small fraction of structured outliers. To address this, we introduce a new meta-algorithm that can take in a…
INSPECTRE: Privately Estimating the Unseen
Jayadev Acharya, Gautam Kamath, Ziteng Sun +1
We develop differentially private methods for estimating various distributional properties. Given a sample from a discrete distribution , some functional , and accuracy and p…
Actively Avoiding Nonsense in Generative Models
Steve Hanneke, Adam Kalai, Gautam Kamath +1
A generative model may generate utter nonsense when it is fit to maximize the likelihood of observed data. This happens due to "model error," i.e., when the true data generating di…