Y Chen, Y Chi, J Fan, C Ma - Foundations and Trends® in …, 2021 - nowpublishers.com
Spectral methods have emerged as a simple yet surprisingly effective approach for extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
C Ma, K Wang, Y Chi, Y Chen - International Conference on …, 2018 - proceedings.mlr.press
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 …
G Li, Y Wei, Y Chi, Y Gu… - Advances in neural …, 2020 - proceedings.neurips.cc
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 …
J Fan, K Li, Y Liao - Annual Review of Financial Economics, 2021 - annualreviews.org
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 …