6 papers
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…
Efficient machine unlearning with minimax optimality
Jingyi Xie, Linjun Zhang, Sai Li
There is a growing demand for efficient data removal to comply with regulations like the GDPR and to mitigate the influence of biased or corrupted data. This has motivated the fiel…
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…
Personalizing black-box models for nonparametric regression with minimax optimality
Sai Li, Linjun Zhang
Recent advances in large-scale models, including deep neural networks and large language models, have substantially improved performance across a wide range of learning tasks. The…
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…