Subsampling focuses on selecting a subsample that can efficiently sketch the information of
the original data in terms of statistical inference. It provides a powerful tool in big data …

The statistical analysis of Randomized Numerical Linear Algebra (RandNLA) algorithms
within the past few years has mostly focused on their performance as point estimators …

Motivated by practical needs such as large-scale learning, we study the impact of adaptivity
constraints to linear contextual bandits, a central problem in online learning and decision …

We address the statistical and optimization impacts of the classical sketch and Hessian
sketch used to approximately solve the Matrix Ridge Regression (MRR) problem. Prior …

Orthogonal subsampling for big data linear regression Page 1 The Annals of Applied Statistics
2021, Vol. 15, No. 3, 1273–1290 https://doi.org/10.1214/21-AOAS1462 © Institute of Mathematical …

The experimental design problem concerns the selection of k points from a potentially large
design pool of p-dimensional vectors, so as to maximize the statistical efficiency regressed …

We develop a simple and efficient algorithm for approximating the John Ellipsoid of a
symmetric polytope. Our algorithm is near optimal in the sense that our time complexity …

We study optimal design problems in which the goal is to choose a set of linear
measurements to obtain the most accurate estimate of an unknown vector. We study the A …

We consider a measurement constrained supervised learning problem, that is,(i) full sample
of the predictors are given;(ii) the response observations are unavailable and expensive to …

We consider computationally tractable methods for the experimental design problem, where
k out of n design points of dimension p are selected so that certain optimality criteria are …