1 citations · 1 across the 2 of their papers we have counts for
7 papers
Changepoint Detection in Complex Models: Cross-Fitting Is Needed
Chengde Qian, Guanghui Wang, Zhaojun Wang +1
Changepoint detection is commonly formulated by minimizing the sum of in-sample losses to quantify the model's overall fit. However, for flexible modeling procedures -- especially…
Reliever: Relieving the Burden of Costly Model Fits for Changepoint Detection
Chengde Qian, Guanghui Wang, Changliang Zou
Changepoint detection typically relies on a grid-search strategy for optimal data segmentation. When model fitting itself is expensive, repeatedly fitting a model on every candidat…
Model-Agnostic and Uncertainty-Aware Dimensionality Reduction in Supervised Learning
Yue Yu, Guanghui Wang, Liu Liu +1
Dimension reduction is a fundamental tool for analyzing high-dimensional data in supervised learning. Traditional methods for estimating intrinsic order often prioritize model-spec…
Empirical Likelihood Meets Prediction-Powered Inference
Guanghui Wang, Mengtao Wen, Changliang Zou
We study inference with a small labeled sample, a large unlabeled sample, and high-quality predictions from an external model. We link prediction-powered inference with empirical l…
Simultaneous Detection and Localization of Mean and Covariance Changes in High Dimensions
Junfeng Cui, Guangming Pan, Guanghui Wang +1
Existing methods for high-dimensional changepoint detection and localization typically focus on changes in either the mean vector or the covariance matrix separately. This separati…
Power Enhancement of Permutation-Augmented Partial-Correlation Tests via Fixed-Row Permutations
Tianyi Wang, Guanghui Wang, Zhaojun Wang +1
Permutation-based partial-correlation tests guarantee finite-sample Type I error control under any fixed design and exchangeable noise, yet their power can collapse when the permut…