142 citations · 842 across the 49 of their papers we have counts for
9 papers · 1 filter
On the Sample Complexity of Privately Learning Unbounded High-Dimensional Gaussians
Ishaq Aden-Ali, Hassan Ashtiani, Gautam Kamath
We provide sample complexity upper bounds for agnostically learning multivariate Gaussians under the constraint of approximate differential privacy. These are the first finite samp…
Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization
Pranav Subramani, Nicholas Vadivelu, Gautam Kamath
A common pain point in differentially private machine learning is the significant runtime overhead incurred when executing Differentially Private Stochastic Gradient Descent (DPSGD…
CoinPress: Practical Private Mean and Covariance Estimation
Sourav Biswas, Yihe Dong, Gautam Kamath +1
We present simple differentially private estimators for the mean and covariance of multivariate sub-Gaussian data that are accurate at small sample sizes. We demonstrate the effect…
A Primer on Private Statistics
Gautam Kamath, Jonathan Ullman
Differentially private statistical estimation has seen a flurry of developments over the last several years. Study has been divided into two schools of thought, focusing on empiric…
The Discrete Gaussian for Differential Privacy
Clément L. Canonne, Gautam Kamath, Thomas Steinke
A key tool for building differentially private systems is adding Gaussian noise to the output of a function evaluated on a sensitive dataset. Unfortunately, using a continuous dist…
PAPRIKA: Private Online False Discovery Rate Control
Wanrong Zhang, Gautam Kamath, Rachel Cummings
In hypothesis testing, a false discovery occurs when a hypothesis is incorrectly rejected due to noise in the sample. When adaptively testing multiple hypotheses, the probability o…
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…
Private Mean Estimation of Heavy-Tailed Distributions
Gautam Kamath, Vikrant Singhal, Jonathan Ullman
We give new upper and lower bounds on the minimax sample complexity of differentially private mean estimation of distributions with bounded -th moments. Roughly speaking, in the…
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…