Substantial progress has been made recently on developing provably accurate and efficient
algorithms for low-rank matrix factorization via nonconvex optimization. While conventional …

Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …

Recent years have seen a flurry of activities in designing provably efficient nonconvex
optimization procedures for solving statistical estimation problems. For various problems like …

Statistical Foundations of Data Science gives a thorough introduction to commonly used
statistical models, contemporary statistical machine learning techniques and algorithms …

We investigate the sample efficiency of reinforcement learning in a $\gamma $-discounted
infinite-horizon Markov decision process (MDP) with state space S and action space A …

The Lasso with general Gaussian designs with applications to hypothesis testing Page 1 The
Annals of Statistics 2023, Vol. 51, No. 5, 2194–2220 https://doi.org/10.1214/23-AOS2327 © …

This article provides a selective overview of the recent developments in factor models and
their applications in econometric learning. We focus on the perspective of the low-rank …

We study a completion problem of broad practical interest: the reconstruction of a low-rank
symmetric tensor from highly incomplete and randomly corrupted observations of its entries …

Missing data is a prevalent, yet often ignored, feature of company fundamentals. In this
paper, we document the structure of missing financial data and show how to systematically …

This paper studies noisy low-rank matrix completion: given partial and noisy entries of a
large low-rank matrix, the goal is to estimate the underlying matrix faithfully and efficiently …