A vast majority of machine learning algorithms train their models and perform inference by
solving optimization problems. In order to capture the learning and prediction problems …

We consider the problem of Robust PCA in the fully and partially observed settings. Without
corruptions, this is the well-known matrix completion problem. From a statistical standpoint …

We consider the problem of testing for a difference in means between clusters of
observations identified via k-means clustering. In this setting, classical hypothesis tests lead …

Inference problems with conjectured statistical-computational gaps are ubiquitous
throughout modern statistics, computer science, statistical physics and discrete probability …

Motivated by applications in neuroimaging analysis, we propose a new regression model,
Sparse TensOr REsponse regression (STORE), with a tensor response and a vector …

Supplement to “CHIME: Clustering of high-dimensional Gaussian mixtures with EM
algorithm and its optimality”. This supplement provides detailed proofs of the Theorem 3.1 …

Abstract The Expectation-Maximization algorithm is perhaps the most broadly used
algorithm for inference of latent variable problems. A theoretical understanding of its …

Modern scientific studies often collect datasets in the form of tensors. These datasets call for
innovative statistical analysis methods. In particular, there is a pressing need for tensor …

We consider joint estimation of multiple graphical models arising from heterogeneous and
high-dimensional observations. Unlike most previous approaches which assume that the …

A line of recent work has analyzed the behavior of the Expectation-Maximization (EM)
algorithm in the well-specified setting, in which the population likelihood is locally strongly …