Multiple Hypothesis Testing

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1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Where Do Two Populations of Persistence Diagrams Differ? Calibrated Local Inference at a Fixed Budget
Oct 6, 2026Pramita Bagchi, Edward Bae, Atish Mitra +3Two-Sample Testing Confidence Region Estimation
The George Washington University · Montana Technological University · University of Ljubljana
How Sensitive Are LLM Leaderboard Claims to Hidden Model Selection?
Sep 23, 2026Chen Yang, Xianyang Zhang, Jun ChenLLM Evaluation Selective Inference
Department of Statistics, Texas A&M University · Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic
Selection-Aware Stress Testing for Interactive Agents
Aug 31, 2026Yang Xu, Chenang Li, Jiefu Zhang +3AI Agent Evaluation Multiple Hypothesis Testing
Purdue University · University of California, Irvine
ARM: Detector-Agnostic Changepoint Attribution with Finite-Sample Error Control
Aug 3, 2026Chenchen Peng, Mixia Wu, Qijing Yan +2Change-Point Detection Multiple Hypothesis Testing
School of Mathematics, Statistics and Mechanics, Beijing University of Technology, Beijing 100124, China · College of Computing and Data Science, Nanyang Technological University, Singapore 639798, Singapore · College of Electrical Engineering, Sichuan University, Chengdu, Sichuan 610065, China
Aggregation of Statistical Evidence under Exchangeability
Jul 17, 2026Antonin Schrab, Rajen Shah, Arthur Gretton +1Sequential Hypothesis Testing Multiple Hypothesis Testing
Department of Computer Science and Technology, University of Cambridge, UK · Statistical Laboratory, University of Cambridge, UK · Gatsby Computational Neuroscience Unit, University College London, UK +1
The Benjamini--Hochberg Procedure Can Fail to Control the FDR for Correlated Two-Sided Gaussian Tests
Jul 13, 2026Edgar DobribanFDR Control Multiple Hypothesis Testing
Department of Statistics and Data Science, University of Pennsylvania.
Finite Resources False Discovery Rate Control in Structured Hypothesis Spaces
Jun 13, 2026Binyamin Perets, Shie MannorFDR Control Multiple Hypothesis Testing
Technion – Israel Institute of Technology · NVIDIA
Measurement Under Selection: Decoy-Calibrated Failure Audits for Language Models
Jun 8, 2026Vyzantinos Repantis, Ameya Gawde, Harshvardhan SinghLLM Evaluation Language Model Error Detection
The Educational Approaches to Virtual Reality Technologies Lab, University of Ioannina, Greece · Meta Platforms
PACE: Anytime-Valid Acceptance Tests for Self-Evolving Agents
Jun 6, 2026Zayx ShawnSequential Hypothesis Testing LLM Agent Reliability
Independent Researcher
Provable Joint Decontamination for Benchmarking Multiple Large Language Models
May 20, 2026Zhenlong Liu, Hao Zeng, Hongxin WeiLLM Evaluation Benchmark Contamination
Department of Statistics and Data Science, Southern University of Science and Technology · Shanghai Innovation Institute
Everywhere Valid Bounds on False Discovery Proportions in Conformal Inference
May 20, 2026Ziang Song, Ying Jin, Emmanuel J. CandèsFDR Control Conformal Prediction
Department of Statistics, Stanford University · Department of Statistics and Data Science, University of Pennsylvania · Department of Mathematics, Stanford University
Controlling False Discovery in Arbitrarily Structured Hypothesis Spaces via Reproducing Kernels
May 17, 2026Binyamin Perets, Shie MannorFDR Control Multiple Hypothesis Testing
Technion – Israel Institute of Technology · NVIDIA
A Regret Perspective on Online Multiple Testing
May 13, 2026Qingyang Hao, Kongchang Zhou, Fang Kong +1FDR Control Regret Minimization
Southern University of Science and Technology
Uncertainty Quantification for LLM-based Code Generation
May 12, 2026Senrong Xu, Yuhao Tan, Yanke Zhou +6Uncertainty Quantification LLM Uncertainty Estimation
State Key Lab of Novel Software Technology, Nanjing University · ETH Zürich · Birkbeck, University of London
MinShap: A Modified Shapley Value Approach for Feature Selection
Apr 16, 2026Chenghui Zheng, Garvesh RaskuttiShapley Value Attribution Feature Selection
Department of Statistics, University of Wisconsin - Madison, Madison, WI 53706
Provable FDR Control for Deep Feature Selection: Deep MLPs and Beyond
Dec 4, 2025Kazuma SawayaFDR Control Feature Selection
The University of Tokyo, Bunkyo, Tokyo, Japan
Reliable Selection of Heterogeneous Treatment Effect Estimators
Nov 23, 2025Jiayi Guo, Zijun GaoCausal Effect Estimation Conditional Average Treatment Effect Estimation
Peking University · Marshall School of Business, University of Southern California
Structural Enforcement of Statistical Rigor in AI-Driven Discovery: A Functional Architecture
Nov 10, 2025Karen SargsyanFDR Control Formal Verification
aInstitute of Chemistry, Academia Sinica, Taipei, Taiwan
Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach
Nov 3, 2025Jacob CarlsonSelective Inference Interpretable ML
Harvard University∗
Multiple Testing of Linear Forms for Noisy Matrix Completion
Dec 1, 2023Wanteng Ma, Lilun Du, Dong Xia +1FDR Control Matrix Completion
Department of Statistics and Data Science, University of Pennsylvania, USA · Department of Decision Analytics and Operations, City University of Hong Kong, Hong Kong · Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong +1

Oct 6, 2026, stat.MEJ/K move · Enter open · S save

Where Do Two Populations of Persistence Diagrams Differ? Calibrated Local Inference at a Fixed Budget

Pramita Bagchi, Edward Bae, Atish Mitra, Alexander D. Silberman+2

The George Washington University · Montana Technological University · University of Ljubljana

Two-Sample Testing Confidence Region Estimation Multiple Hypothesis Testing Topological Data Analysis

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Many two-sample tests for populations of persistence diagrams assess global differences without identifying the regions of the birth-death plane that contribute to them. We study simultaneous inference for local mean contrasts when the number of available diagrams is fixed. They are differences in expected weighted feature mass within

\ell_\infty

neighborhoods at several centers and radii. We estimate these contrasts using additive landmark responses. A Gaussian multiplier bootstrap calibrates simultaneous confidence intervals while allowing unequal group covariances. The neighborhoods whose intervals exclude zero form a map with approximate family-wise error control, and selecting a subset of original intervals for display preserves their joint coverage guarantee. On the simultaneous coverage event, every reported neighborhood lies within twice its radius of the support of the mean-measure difference. A geometric result gives sufficient radius conditions for a displaced feature to produce a nonzero contrast. A comparison of sufficient detection thresholds quantifies the tradeoff between reducing the number of tested coordinates and reserving observations for an independent pilot. In simulations with 40 to 120 diagrams per class, the bands achieved 94%-98% simultaneous coverage under both the strict null and equal means with unequal covariances. In the latter setting, a permutation maximum and the pooled-t implementation of the two-stage persistence-image test of Moon and Lazar rejected in up to 32% and 26% of runs, respectively. In the fixed-budget simulations, spending a third of the observations on a pilot to choose landmarks or radii located changes less often than a prespecified grid at a single radius. On the MUTAG benchmark, the localized region concentrates on rings of fused-ring systems, an exploratory reading.

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