We study the relationship between adversarial robustness and differential privacy in high-
dimensional algorithmic statistics. We give the first black-box reduction from privacy to …

In this work, we give efficient algorithms for privately estimating a Gaussian distribution in
both pure and approximate differential privacy (DP) models with optimal dependence on the …

We study the problem of high-dimensional sparse mean estimation in the presence of an
$\epsilon $-fraction of adversarial outliers. Prior work obtained sample and computationally …

We prove that there is a universal constant $ C> 0$ so that for every $ d\in\mathbb N $, every
centered subgaussian distribution $\mathcal D $ on $\mathbb R^ d $, and every even …

We give the first polynomial time algorithm for list-decodable covariance estimation. For any
α> 0, our algorithm takes input a sample Y⊆ d of size n≥ d poly (1/α) obtained by …

We revisit the problem of estimating the mean of a high-dimensional distribution in the
presence of an ε-fraction of adversarial outliers. When ε is at most some sufficiently small …

We study the problem of robustly estimating the mean or location parameter without moment
assumptions. Known computationally efficient algorithms rely on strong distributional …

Training modern neural networks or models typically requires averaging over a sample of
high-dimensional vectors. Poisoning attacks can skew or bias the average vectors used to …

A celebrated connection in the interface of online learning and game theory establishes that
players minimizing swap regret converge to correlated equilibria (CE)--a seminal game …

We develop a technique to design efficiently computable estimators for sparse linear
regression in the simultaneous presence of two adversaries: oblivious and adaptive. We …