Author

C. Lippert

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#artificial intelligence Preprint Oct 2026

Two-Sample Testing via Path-based Inference

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 inference. We develop this idea for two-sample testing, the problem of deciding whether the same distribution generated two finite datasets...

Eshant English, Wei-Cheng Lai, Yan-Feng Yang et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Embedded Conditional Independence Tests for Large Language Model Generated Text with an Application to German Parliament Speeches

Embedded CITs (eCITs), which embed X and Z and apply an existing CIT to the resulting representations and to the resulting representations, are proposed and it is shown that sufficiency weakens to mean sufficiency when the embedded test targets conditional mean independence.

Marco Simnacher, Georg Keilbar, B. König et al. · 1 citation