7 papers
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…
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…
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…
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…
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…