17 citations · 57 across the 9 of their papers we have counts for
13 papers
Stance Drift: How AI-mediated Communication Distorts Our Message
Lingchong Liu, Yanfei Zhou, Jacob Bien +3
Large language models (LLMs) increasingly mediate human communication, from drafting emails to summarizing scientific reports, yet whether they faithfully preserve a speaker's posi…
Is the regression -test doubly robust?
Lucy Xia, Oliver Y. Feng, Yang Feng +2
We study the robustness of the -test in random design linear models, and reach a somewhat nuanced conclusion. On the positive side, one of our main results is that the size of t…
Neyman-Pearson and equal opportunity: when efficiency meets fairness in classification
Jianqing Fan, Xin Tong, Yanhui Wu +2
Organizations often rely on statistical algorithms to make socially and economically impactful decisions. We must address the fairness issues in these important automated decisions…
Robust Unsupervised Multi-task and Transfer Learning on Gaussian Mixture Models
Ye Tian, Haolei Weng, Lucy Xia +1
Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM).…
Inference for linear functionals of high-dimensional longitudinal proteomics data using generalized estimating equations
Lu Xia, Ali Shojaie
Regression analysis of correlated data, where multiple correlated responses are recorded on the same unit, is ubiquitous in many scientific areas. With the advent of new technologi…
Non-splitting Neyman-Pearson Classifiers
Jingming Wang, Lucy Xia, Zhigang Bao +1
The Neyman-Pearson (NP) binary classification paradigm constrains the more severe type of error (e.g., the type I error) under a preferred level while minimizing the other (e.g., t…