Permutation Testing

Latest papers

12 papers3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Two-Sample Testing via Path-based Inference
Oct 5, 2026Eshant English, Wei-Cheng Lai, Yanfeng Yang +3Two-Sample Testing Permutation Testing
The University of Tokyo, Tokyo, Japan · Hasso Plattner Institute for Digital Engineering, Germany · Institute of Statistical Mathematics, Japan +3
Cheap and Powerful Tests for Supervised Subspaces: Per-Component Inference for PLS
Sep 28, 2026Paweł Lenartowicz, Hubert PlisieckiPermutation Testing Statistical Hypothesis Testing
Society for Open Science (Stowarzyszenie na Rzecz Otwartej Nauki) Centre for Brain Research, Jagiellonian University · Society for Open Science (Stowarzyszenie na Rzecz Otwartej Nauki) IDEAS Research Institute
The Null Is the Hard Part: Exact Tests for Memorization in Generative Models
Sep 23, 2026Sushovan Majhi, Pramita BagchiPermutation Testing Memorization in Generative Models
Data Science, George Washington University, USA · Statistics, George Washington University, USA
LOTTERY: Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry
Jun 7, 2026Xunye Tian, Zhijian Zhou, Liuhua Peng +1Two-Sample Testing Permutation Testing
University of Melbourne · Maincode
When prompt perturbations break your A/B test: A valid statistical test for generative surveying
May 26, 2026Hayden Helm, Carey PriebePrompt Sensitivity Permutation Testing
Helivan · Johns Hopkins University
A Family of Divergence Measures for Evaluating the Reconstruction Quality of Explainable Ensemble Trees
May 19, 2026Massimo Aria, Agostino Gnasso, Carmela IorioSurrogate Modeling Permutation Testing
Department of Economics and Statistics, University of Naples2026 Federico II, Naples, Italy
Accurate and Efficient Statistical Testing for Word Semantic Breadth
May 8, 2026Yo EharaTwo-Sample Testing Permutation Testing
Tokyo Gakugei University
PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Data
May 6, 2026Thomas S. Robinson, Ranjit LallPermutation Testing Incomplete Data Imputation
Department of Methodology London School of Economics and Political Science London, UK · Department of Politics and International Relations University of OxfordMay Oxford, UK
Non-Stationarity Breaks Permutation Surrogates in Multi-Agent Reinforcement Learning: Diagnosis and Remedies
Apr 26, 2026Nikolaos Al. Papadopoulos, Konstantinos E. PsannisPermutation Testing Mutual Information Estimation
Department of Applied Informatics, University of Macedonia, Egnatia 156, 54636 Thessaloniki, Greece
Statistical Properties of the King Wen Sequence: An Anti-Habituation Structure That Does Not Improve Neural Network Training
Apr 10, 2026Augustin ChanPermutation Testing Neural Network Optimization
Independent Researcher
A statistical perspective on transformers for small longitudinal cohort data
Feb 18, 2026Kiana Farhadyar, Maren Hackenberg, Kira Ahrens +10Permutation Testing Time Series Transformers
Institute of Medical Biometry and Statistics (IMBI), University of Freiburg, Freiburg, Germany · Freiburg Center for Data Analysis, Modeling and AI, University of Freiburg, Freiburg, Germany · Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany +6
Revisiting the Platonic Representation Hypothesis: An Aristotelian View
Feb 16, 2026Fabian Gröger, Shuo Wen, Maria BrbićPermutation Testing Representational Similarity Analysis
1EPFL · University of Basel · 3HSLU

Oct 5, 2026J/K move · Enter open · S save

Two-Sample Testing via Path-based Inference

Eshant English, Wei-Cheng Lai, Yanfeng Yang, Kenji Fukumizu+2

The University of Tokyo, Tokyo, Japan · Hasso Plattner Institute for Digital Engineering, Germany · Institute of Statistical Mathematics, Japan+3

Two-Sample Testing Permutation Testing Statistical Hypothesis Testing

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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. Using stochastic interpolants, we connect both distributions to a shared Gaussian bottleneck, so that each half of the resulting path is a Gaussian channel acting on a single population. We prove that the null hypothesis holds if and only if the population denoiser, or equivalently, the velocity fields of the two halves, coincide at any single noise level, which amounts to a reflection symmetry of the path about the bottleneck. Deviations from this symmetry yield a continuum of two-sample witnesses, which we estimate via held-out regression risks on learned denoisers and velocities and aggregate along the path; under an information-theoretic weighting, the aggregated discrepancy equals the Jeffreys divergence between the noise-smoothed distributions. Calibrating the resulting statistics by permutation yields tests that are valid in finite samples for any trained networks and consistent when the fields are learned accurately. On a synthetic benchmark and three image benchmarks, the proposed tests improve power over the strongest baseline by up to 33 percentage points at an equal total sample budget, with the best choice of regression representation and path weighting depending on the data modality. These results show that generative paths provide a principled representation for statistical testing, extending stochastic-interpolant models beyond generation.