9 papers
Online Individualized Treatment Selection under Partial Feedback: False Discovery Control and Minimax Optimality
Runzhe Gu, Zinan Zhao, Wenguang Sun +1
We study online individualized treatment selection when only the outcome under the assigned treatment is observed. Our objective is to preferentially treat individuals whose potent…
Learning a directed acyclic graph with additive heteroscedastic errors
Xintao Xia, Li Chen, Yue Hu +1
This paper studies causal discovery for a directed acyclic graph under a structural equation model with additive heteroscedastic errors. We first establish new identifiability resu…
Model Checking for Regressions Based on Weighted Residual Processes with Diverging Number of Predictors
Yue Hu, Haiqi Li, Xintao Xia
The integrated conditional moment (ICM) test is a classical and widely used method for assessing the adequacy of regression models. Although it performs well in fixed-dimension set…
A Statistical Framework for Alignment with Biased AI Feedback
Xintao Xia, Zhiqiu Xia, Linjun Zhang +1
Modern alignment pipelines are increasingly replacing expensive human preference labels with evaluations from large language models (LLM-as-Judge). However, AI labels can be system…
Multiply Robust Inference of Average Treatment Effects by High-dimensional Empirical Likelihood
Xintao Xia, Yumou Qiu
In this paper, we develop a multiply robust inference procedure of the average treatment effect (ATE) for data with high-dimensional covariates. We consider the case where it is di…
Statistical Inference for Differentially Private Stochastic Gradient Descent
Xintao Xia, Linjun Zhang, Zhanrui Cai
Privacy preservation in machine learning, particularly through Differentially Private Stochastic Gradient Descent (DP-SGD), is critical for sensitive data analysis. However, existi…
Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm
Xintao Xia, Linjun Zhang, Zhanrui Cai
Privacy preservation has become a critical concern in high-dimensional data analysis due to the growing prevalence of data-driven applications. Since its proposal, sliced inverse r…
Differentially Private Estimation and Inference in High-Dimensional Regression with FDR Control
Zhanrui Cai, Sai Li, Xintao Xia +1
This paper proposes new methodologies for conducting practical differentially private (DP) estimation and inference in high-dimensional linear regression. We first introduce a DP B…
Adaptive False Discovery Rate Control with Privacy Guarantee
Xintao Xia, Zhanrui Cai
Differentially private multiple testing procedures can protect the information of individuals used in hypothesis tests while guaranteeing a small fraction of false discoveries. In…