activity
20242026
most citedHypothesis testing with e-values

18 citations · 23 across the 14 of their papers we have counts for

collaborators

17 papers

cs.RO2026

Not All Uncertainty Matters: Simulation-in-the-Loop Fast-Slow Reasoning for Decision-Critical Autonomous Driving System

Jiayi Chen, Shuai Wang, Guangxu Zhu +3

Large vision-language models (VLMs) provide powerful open-world perception and reasoning for autonomous driving, but their high computational cost and inference latency make contin…

math.ST2026

The exact dimensional threshold for Spearman rank-correlation compatibility

Ruodu Wang, Zhenyuan Zhang

We show that, for a given dimension, the set of Spearman's rank correlation matrices and that of linear correlation matrices coincide if and only if the dimension is no larger than…

math.ST2026

Gaffke's confidence interval for the mean of bounded data is inadmissible but asymptotically efficient

Jiahao Ming, Aaditya Ramdas, Yi Shen +2

Given observations , Gaffke (2005) defined \[ K_n(\mathbf x)=\mathbb{P}_{\mathbf D}\!\left\{\sum_{i=1}^n x_iD_i\le 1\right\}, \qquad (D_0,D_1,\ldots,D_n)…

stat.ME2026

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

Liulei Sun, Ruodu Wang

The false discovery rate (FDR) is the most widely used error metric in modern multiple testing. We provide a comprehensive analysis of the admissibility of e-value-based procedures…

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 t…

stat.ME2026

Combining e-values using demi-supermartingales

Jiahao Ming, Aaditya Ramdas, Yi Shen +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.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.ML2026

Online monotone density estimation and log-optimal calibration

Rohan Hore, Ruodu Wang, Aaditya Ramdas

We study the problem of online monotone density estimation, where density estimators must be constructed in a predictable manner from sequentially observed data. We propose two onl…

econ.TH2025

Invariant Modeling for Joint Distributions

Christopher P. Chambers, Yusufcan Masatlioglu, Ruodu Wang

A common theme underlying many problems in statistics and economics involves the determination of a systematic method of selecting a joint distribution consistent with a specified…

q-fin.RM2025

Eliciting reference measures of law-invariant functionals

Felix-Benedikt Liebrich, Ruodu Wang

Law-invariant functionals are central to risk management and assign identical values to random prospects sharing the same distribution under an atomless reference probability measu…

q-fin.RM2025

Rating criteria, risk measures, and risk consistency

Nan Guo, Ruodu Wang, Chenxi Xia +1

Categorical ratings are widely used by investors to screen financial products, such as defaultable securities and investment funds. We model rating criteria as discrete transforms…

math.PR2025

Quadratic-form Optimal Transport

Ruodu Wang, Zhenyuan Zhang

We introduce the framework of quadratic-form optimal transport (QOT), whose transport cost has the form for some coupling between two m…

math.ST2024★ 18 cited

Hypothesis testing with e-values

Aaditya Ramdas, Ruodu Wang

This book is written to offer a humble, but unified, treatment of e-values in hypothesis testing. It is organized into three parts: Fundamental Concepts, Core Ideas, and Advanced T…

math.ST2024

The only admissible way of merging arbitrary e-values

Ruodu Wang

We prove that the only admissible way of merging arbitrary e-values is to use a weighted arithmetic average. This result completes the picture of merging methods for e-values, and…

stat.ME2024

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…

math.ST2024

Improved thresholds for e-values

Christopher Blier-Wong, Ruodu Wang

The rejection threshold used for e-values and e-processes is by default set to for a guaranteed type-I error control at , based on Markov's and Ville's inequalities. This…

math.ST2024★ 5 cited

Combining exchangeable p-values

Matteo Gasparin, Ruodu Wang, Aaditya Ramdas

The problem of combining p-values is an old and fundamental one, and the classic assumption of independence is often violated or unverifiable in many applications. There are many w…