5 papers
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…
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…
Understanding Representation Learnability of Nonlinear Self-Supervised Learning
Ruofeng Yang, Xiangyuan Li, Bo Jiang +1
Self-supervised learning (SSL) has empirically shown its data representation learnability in many downstream tasks. There are only a few theoretical works on data representation le…
Nearest-Neighbor Sampling Based Conditional Independence Testing
Shuai Li, Ziqi Chen, Hongtu Zhu +2
The conditional randomization test (CRT) was recently proposed to test whether two random variables X and Y are conditionally independent given random variables Z. The CRT assumes…