In statistical learning and analysis from shared data, which is increasingly widely adopted in
platforms such as federated learning and meta-learning, there are two major concerns …

We obtain robust and computationally efficient estimators for learning several linear models
that achieve statistically optimal convergence rate under minimal distributional assumptions …

We develop a general framework for finding approximately-optimal preconditioners for
solving linear systems. Leveraging this framework we obtain improved runtimes for …

We study the fundamental problems of Gaussian mean estimation and linear regression with
Gaussian covariates in the presence of Huber contamination. Our main contribution is the …

We investigate a variation of the 3D registration problem, named multi-model 3D
registration. In the multi-model registration problem, we are given two point clouds picturing …

Linear dynamical systems are the foundational statistical model upon which control theory is
built. Both the celebrated Kalman filter and the linear quadratic regulator require knowledge …

We develop two methods for the following fundamental statistical task: given an $\eps $-
corrupted set of $ n $ samples from a $ d $-dimensional sub-Gaussian distribution, return an …

A common challenge faced in practical supervised learning, such as medical image
processing and robotic interactions, is that there are plenty of tasks but each task cannot …

We study the problem of list-decodable sparse mean estimation. Specifically, for a
parameter $\alpha\in (0, 1/2) $, we are given $ m $ points in $\mathbb {R}^ n $, $\lfloor\alpha …

We investigate area convexity [Sherman17], a mysterious tool introduced to tackle
optimization problems under the challenging $\ell_\infty $ geometry. We develop a deeper …