An optimal statistical and computational framework for generalized tensor estimation Page 1 The
Annals of Statistics 2022, Vol. 50, No. 1, 1–29 https://doi.org/10.1214/21-AOS2061 © Institute of …

In the stochastic contextual low-rank matrix bandit problem, the expected reward of an action
is given by the inner product between the action's feature matrix and some fixed, but initially …

In a low-rank linear bandit problem, the reward of an action (represented by a matrix of size
$ d_1\times d_2 $) is the inner product between the action and an unknown low-rank matrix …

In this paper, we develop a novel procedure for low-rank tensor regression, namely
Importance Sketching Low-rank Estimation for Tensors (ISLET). The central idea behind …

This paper studies panel data models with interactive fixed effects where the regressors are
allowed to be correlated with the idiosyncratic error terms. We propose a two-step profile …

In this paper, we propose a uniformly dithered 1-bit quantization scheme for high-
dimensional statistical estimation. The scheme contains truncation, dithering, and …

Inference for low-rank tensors-no need to debias Page 1 The Annals of Statistics 2022, Vol. 50,
No. 2, 1220–1245 https://doi.org/10.1214/21-AOS2146 © Institute of Mathematical Statistics …

This article focuses on the relationship between a measure of creativity and the human brain
network for subjects in a brain connectome dataset obtained using a diffusion weighted …

This paper studies high-dimensional vector autoregressions (VARs) augmented with
common factors that allow for strong cross-sectional dependence. Models of this type …

We consider a general trace regression model with multiple structural changes and propose
a universal approach for simultaneous exact or near-low-rank matrix recovery and change …