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stat.ME2026

Admissibility and Complete Classes for False Discovery Rate Control with E-values

Liulei Sun, Ruodu Wang

The paper analyzes the admissibility of e‑value based procedures for controlling the false discovery rate, showing that weighted‑mean e‑Benjamini‑Hochberg methods form a complete c…

stat.ME2026

Online LLM watermark detection via e-processes

Weijie Su, Ruodu Wang, Zinan Zhao

Watermarking for large language models (LLMs) has emerged as an effective tool for distinguishing AI-generated text from human-written content. Statistically, watermark schemes ind…

stat.ME2026

Tiny but uniform improvements of adaptive BH procedures via compound e-values

Nikolaos Ignatiadis, Ruodu Wang, Aaditya Ramdas

After the seminal Benjamini-Hochberg (BH) procedure for controlling the false discovery rate (FDR) was proposed, dozens of papers have attempted to improve its power by adapting to…

stat.ME2026

Combining e-values using demi-supermartingales

Jiahao Ming, Yi Shen, Aaditya Ramdas +2

We present a new method for combining e-variables through demi-supermartingales, which settles an old conjecture in the literature on nonparametric mean testing. It also provides a…

stat.ME2025

Asymptotic and compound e-values: multiple testing and empirical Bayes

Nikolaos Ignatiadis, Ruodu Wang, Aaditya Ramdas

We explicitly define the notions of (bona fide, approximate or asymptotic) compound p-values and e-values, which have been implicitly presented and used in the recent multiple test…