1 citations · 1 across the 7 of their papers we have counts for
7 papers
Mitigating the Curse of Dimensionality in Uniform Convergence of Deep Neural Networks via Smooth Activations
Yizhe Ding, Runze Li, Jia Liu +1
This paper establishes a theoretical framework for the uniform convergence of smoothly activated deep neural network (DNN) estimators. While standard ReLU networks achieve minimax-…
New Empirical Process Tools and Their Applications to Robust Deep ReLU Networks and Phase Transitions for Nonparametric Regression
Yizhe Ding, Runze Li, Lingzhou Xue
This paper introduces new empirical process tools for analyzing a broad class of statistical learning models under heavy-tailed noise and complex function classes. Our primary cont…
Strongly Consistent Community Detection in Popularity Adjusted Block Models
Quan Yuan, Binghui Liu, Danning Li +1
The Popularity Adjusted Block Model (PABM) provides a flexible framework for community detection in network data by allowing heterogeneous node popularity across communities. Howev…
Statistical Inference for High-Dimensional Robust Linear Regression Models via Recursive Online-Score Estimation
Dian Zheng, Lingzhou Xue
This paper introduces a novel framework for estimation and inference in penalized M-estimators applied to robust high-dimensional linear regression models. Traditional methods for…
Statistical Convergence Rates of Optimal Transport Map Estimation between General Distributions
Yizhe Ding, Runze Li, Lingzhou Xue
This paper studies the convergence rates of optimal transport (OT) map estimators, a topic of growing interest in statistics, machine learning, and various scientific fields. Despi…
Hypothesis Testing for High-Dimensional Matrix-Valued Data
Shijie Cui, Danning Li, Runze Li +1
This paper addresses hypothesis testing for the mean of matrix-valued data in high-dimensional settings. We investigate the minimum discrepancy test, originally proposed by Cragg (…
Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees
Xin Yu, Zelin He, Ying Sun +2
Personalized federated learning (PFL) offers a flexible framework for aggregating information across distributed clients with heterogeneous data. This work considers a personalized…