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stat.ML2026
Two-Sample Testing via Generative Processes
Eshant English, Kenji Fukumizu, Taiji Suzuki
Deciding whether two samples come from the same distribution is a classical problem in statistics, and generative transport offers a new way to approach it. We build a stochastic i…
stat.ML2026
Two-Sample Testing via Path-based Inference
Eshant English, Wei-Cheng Lai, Yanfeng Yang +3
Modern deep generative models are primarily studied for their ability to generate realistic samples, yet the generative dynamics they learn can also serve as objects of statistical…
stat.ML2026
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-…