Statistical Hypothesis Testing

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6 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

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A nonparametric two-sample test using a parametric integral probability metric
Jun 15, 2026Yuha Park, Yongdai KimTwo-Sample Testing Statistical Learning Theory
Department of Mathematics, University of Hamburg, Hamburg, Germany. · Department of Statistics, Seoul National University, Seoul, Republic of Korea.
Ablation, Statistical Inference, and Validation for KV-Cache Compression
Jun 14, 2026Paolo D'Alberto, Ashish Siarasao, Elliott Delaye +1KV Caching Rotation-Based Quantization
Advanced Micro Devices, Inc.
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
From Persistence to Survival: Hypothesis Testing, Effect Sizes and Vectorisation for Topological Features
Jun 10, 2026Juliette Murris, Bernadette Stolz, Karsten BorgwardtTwo-Sample Testing Persistent Homology
Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried, Germany
Taming Perception Jitter: Uncertainty-Aware LiDAR Object Detection for Reliable Motion Classification
Jun 8, 2026Cornelius Schröder, Žygimantas Marcinkus, Markus LienkampLidar-Based 3D Object Detection Uncertainty Quantification
Institute for Automotive Engineering, Munich Institute of Robotics and Machine Intelligence, School of Engineering and Design, Technical University of Munich
LOTTERY: Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry
Jun 7, 2026Xunye Tian, Zhijian Zhou, Liuhua Peng +1Two-Sample Testing Statistical Hypothesis Testing
University of Melbourne · Maincode
Sharp Low-Degree Thresholds for Planted-vs-Planted Testing
Jun 3, 2026Anda Skeja, Daniel Gutiérrez Espinoza, Fiona Skerman +1Two-Sample Testing Graph Clustering
Department of Mathematics, Uppsala University · Department of Mathematics, University of California, Davis
Counterfactual Explanations for Deep Two-Sample Testing
May 29, 2026Wei-Cheng Lai, Marco Simnacher, Christoph LippertTwo-Sample Testing Counterfactual Explanations
Digital Health and Machine Learning Hasso-Plattner-Institute, University of Potsdam Potsdam, Germany · Chair of Statistics Humboldt-Universität zu Berlin Berlin, Germany · Hasso Plattner Institute for Digital Health at Mount Sinai Icahn School of Medicine at Mount Sinai New York, United States of America
Few-Shot Resampling for Scalable Statistically-Sound Data Mining
May 29, 2026Leonardo Pellegrina, Fabio VandinFDR Control Statistical Hypothesis Testing
Department of Information Engineering, University of Padova Padova, Italy
Resolution Diagnostics for Paired LLM Evaluation
May 28, 2026Anany KotawalaLLM Evaluation Pairwise Comparison
Princeton University, Princeton, NJ, USA.
Optimal Rates for Differentially Private Hypothesis Testing with E-values
May 27, 2026Ben Jacobsen, Tomas Gonzalez, Gavin Brown +2E-Values Differential Privacy
University of Wisconsin-Madison · Carnegie Mellon University · University of Wisconsin–Madison
Semi-Supervised Hypothesis Testing by Betting on Predictions
May 27, 2026Yaniv Tenzer, Elad Tolochinsky, Yaniv RomanoE-Values Semi-Supervised Learning
Department of Computer Science, Technion – Israel Institute of Technology · Department of Electrical and Computer Engineering, Technion – Israel Institute of Technology
Entropy Distribution as a Fingerprint for Hallucinations in Generative Models
May 27, 2026Mattia J. Villani, Pranav Deshpande, Akshay Seshadri +2Language Model Error Detection Language Model Generation Evaluation
Global Technology Applied Research, JPMorganChase, New York, NY 10001, USA
Semiparametrically Efficient Inference for Kernel Measures of Noise Heterogeneity
May 26, 2026Jakub Wornbard, Zikai Shen, Dimitri Meunier +1Semiparametric Inference Kernel Methods
University College London
When prompt perturbations break your A/B test: A valid statistical test for generative surveying
May 26, 2026Hayden Helm, Carey PriebePrompt Sensitivity A/b Testing
Helivan · Johns Hopkins University
Pre-Registering the Detectable Effect: A Paired-MDE Budget for 4-bit Quantization Benchmarks, with a Pilot Audit
May 25, 2026Zexin Zhuang, Yanhang Li, Zhichao FanLLM Quantization Statistical Power Analysis
1Southern Methodist University · 2Northeastern University · University of Illinois Urbana-Champaign
Nyström Kernel Stein Discrepancy Tests
May 24, 2026Florian Kalinke, Zoltán Szabó, Bharath K. SriperumbudurStatistical Hypothesis Testing
Chair of Information Systems Karlsruhe Institute of Technology Am Fasanengarten 5, 76131 Karlsruhe, Germany · Department of Statistics London School of Economics Houghton Street, London, WC2A 2AE, UK · Department of Statistics The Pennsylvania State UniversityMay University Park, PA 16802, USA
On the Sample Complexity of Robust Binary Hypothesis Testing
May 23, 2026Shankar Vallinayagam, Ankit Pensia, Varun JogSample Complexity Data Contamination
Department of Pure Mathematics and Mathematical Statistics, University of Cambridge · Department of Statistics, Carnegie Mellon University
MARS: Magnitude-Aware Rank Statistics
May 22, 2026Muhammad Rajabinasab, Afsaneh M. Nejad, Arthur ZimekStatistical Hypothesis Testing
University of Southern Denmark, Odense, Denmark
A Martingale Kernel Independence Test
May 21, 2026Felix Laumann, Zhaolu Liu, Mauricio BarahonaMaximum Mean Discrepancy Kernel Methods
Imperial College London
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
Sinkhorn Treatment Effects: A Causal Optimal Transport Measure
May 8, 2026Medha Agarwal, Alex LuedtkeCausal Effect Estimation Causal Inference
Department of Statistics, University of Washington, Seattle, WA, USA · Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
Accurate and Efficient Statistical Testing for Word Semantic Breadth
May 8, 2026Yo EharaTwo-Sample Testing Pairwise Comparison
Tokyo Gakugei University
Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
May 8, 2026Houssam Zenati, Arthur GrettonSemiparametric Inference Causal Effect Estimation
1Gatsby Computational Neuroscience Unit, University College London
CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency
May 7, 2026Hirofumi Ota, Naoto Iwase, Yuki Ichihara +2LLM Evaluation Anytime-Valid Inference
arXiv:2605.05873v1
A Semi-Supervised Kernel Two-Sample Test
May 3, 2026Gyumin Lee, Shubhanshu Shekhar, Ilmun KimTwo-Sample Testing Kernel Methods
Dept. of Statistics Pennsylvania State University · Dept. of EECS University of Michigan · Dept. of Mathematical Sciences KAIST
Mean Testing under Truncation beyond Gaussian
May 2, 2026Yuhao Wang, Roberto Imbuzeiro Oliveira, Themis GouleakisSample Complexity Information-Theoretic Lower Bounds
Amazon · IMPA, Rio de Janeiro, RJ, Brazil · Nanyang Technological University
Finite-Sample Analysis of Elimination in Active Hypothesis Testing
May 1, 2026Ziyuan Lin, Hoang Ngoc Nguyen, Jie Xu +1Sequential Hypothesis Testing Statistical Hypothesis Testing
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
Deep-testing: the case of dependence detection
Apr 29, 2026Gery Geenens, Pierre Lafaye de Micheaux, Ivan Muyun ZouStatistical Hypothesis Testing
School of Mathematics and statistics, UNSW Sydney, Australia

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A nonparametric two-sample test using a parametric integral probability metric

Yuha Park, Yongdai Kim

Department of Mathematics, University of Hamburg, Hamburg, Germany. · Department of Statistics, Seoul National University, Seoul, Republic of Korea.

Two-Sample Testing Statistical Learning Theory Integral Probability Metrics Statistical Hypothesis Testing

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Detecting distributional differences between two independent samples is a fundamental problem in statistics and machine learning. Nonparametric two-sample testing provides a principled framework for determining whether two samples are drawn from the same underlying distribution, without assuming any specific parametric form for the distribution. In this study, we propose a new two-sample test statistic based on a newly introduced integral probability metric (IPM), using a specially designed parametric discriminator class with a single node of a neural network. We show that the resulting test statistic, called PReLU-IPM, is nonparametric and establish theoretical guarantees for the associated two-sample testing procedure, PReLU-TST, including its consistency and asymptotical equivalence to nonparametric IPM-based tests under regularity conditions. By analyzing multiple simulated and real benchmark datasets, we demonstrate that PReLU-TST achieves higher power across a range of alternatives or performs comparably to its competitors, for finite samples.

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