8 papers
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…
FAIRM: Learning invariant representations for algorithmic fairness and domain generalization with minimax optimality
Sai Li, Linjun Zhang
Machine learning methods often assume that the test data have the same distribution as the training data. However, this assumption may not hold due to multiple levels of heterogene…
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…
Multi-dimensional domain generalization with low-rank structures
Sai Li, Linjun Zhang
In conventional statistical and machine learning methods, it is typically assumed that the test data are identically distributed with the training data. However, this assumption do…