Many questions of fundamental interest in today's science can be formulated as inference
problems: some partial, or noisy, observations are performed over a set of variables and the …

The principal submatrix localization problem deals with recovering a K× K principal
submatrix of elevated mean µ in a large n× n symmetric matrix subject to additive standard …

Over the last decade or so, Approximate Message Passing (AMP) algorithms have become
extremely popular in various structured high-dimensional statistical problems. Although the …

In this paper, we propose a general framework for tensor singular value decomposition
(tensor singular value decomposition (SVD)), which focuses on the methodology and theory …

This paper surveys some recent developments in fundamental limits and optimal algorithms
for network analysis. We focus on minimax optimal rates in three fundamental problems of …

We introduce a framework for proving lower bounds on computational problems over
distributions against algorithms that can be implemented using access to a statistical query …

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

Estimation of Wasserstein distances in the Spiked Transport Model Page 1 Bernoulli 28(4),
2022, 2663–2688 https://doi.org/10.3150/21-BEJ1433 Estimation of Wasserstein distances …

This paper considers the matrix completion problem. We show that it is not necessary to
assume joint incoherence, which is a standard but unintuitive and restrictive condition that is …

Many high-dimensional statistical inference problems are believed to possess inherent
computational hardness. Various frameworks have been proposed to give rigorous …