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Domain-Adapted Diffusion Models for Conditional Independence Testing
Yanfeng Yang, Junda Zhao, Yijie Gao +8
Conditional independence (CI) is a fundamental concept in statistics and machine learning. Recent advances in conditional generative modeling provide flexible tools for generative-…
Conditionally Whitened Generative Models for Probabilistic Time Series Forecasting
Yanfeng Yang, Siwei Chen, Pingping Hu +6
Probabilistic forecasting of multivariate time series is challenging due to non-stationarity, inter-variable dependencies, and distribution shifts. While recent diffusion and flow…
Nonlinear Sparse Generalized Canonical Correlation Analysis for Multi-view High-dimensional Data
Rong Wu, Ziqi Chen, Gen Li +1
Motivation: Biomedical studies increasingly produce multi-view high-dimensional datasets (e.g., multi-omics) that demand integrative analysis. Existing canonical correlation analys…
Conditional Diffusion Models Based Conditional Independence Testing
Yanfeng Yang, Shuai Li, Yingjie Zhang +4
Conditional independence (CI) testing is a fundamental task in modern statistics and machine learning. The conditional randomization test (CRT) was recently introduced to test whet…