1 citations · 2 across the 11 of their papers we have counts for
12 papers
LeHami: Symplectic Latent Dynamics for World Models
Romina Yalovetzky, Randall Balestriero, Yann LeCun
Joint-embedding predictive architectures (JEPAs) learn world models directly in latent space, but unconstrained autoregressive predictors may contract the latent distribution over…
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…
ART: Distribution-Free and Model-Agnostic Changepoint Detection with Finite-Sample Guarantees
Xiaolong Cui, Haoyu Geng, Guanghui Wang +2
We introduce ART, a distribution-free and model-agnostic framework for changepoint detection that provides finite-sample guarantees. ART transforms independent observations into re…
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…
TUNE: Algorithm-Agnostic Inference after Changepoint Detection
Yinxu Jia, Jixuan Liu, Guanghui Wang +2
In multiple changepoint analysis, assessing the uncertainty of detected changepoints is crucial for enhancing detection reliability -- a topic that has garnered significant attenti…
Uncertainty Quantification for Data-Driven Change-Point Learning via Cross-Validation
Hui Chen, Yinxu Jia, Guanghui Wang +1
Accurately detecting multiple change-points is critical for various applications, but determining the optimal number of change-points remains a challenge. Existing approaches based…
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…
Zipper: Addressing degeneracy in algorithm-agnostic inference
Geng Chen, Yinxu Jia, Guanghui Wang +1
The widespread use of black box prediction methods has sparked an increasing interest in algorithm/model-agnostic approaches for quantifying goodness-of-fit, with direct ties to sp…
Computationally efficient and data-adaptive changepoint inference in high dimension
Guanghui Wang, Long Feng
High-dimensional changepoint inference that adapts to various change patterns has received much attention recently. We propose a simple, fast yet effective approach for adaptive ch…