Papers Two-sample testing
“Two-sample testing” 태그가 달린 논문 360편 · 필터 해제
Zero-Flow Two-Sample Tests
We propose a new approach to two-sample testing for deciding whether two sets of samples are drawn from the same distribution. The test is built on a statistical discrepancy based on the zero-flow criterion, termed zero-…
Two-sample testingPost Hoc Inference for Component Attribution in Multivariate Change-Point Detection
We consider the post-detection analysis of change-points for multivariate time series, with the goal of identifying which coordinates are responsible for a detected change. After a change-point has been located by an off…
Two-sample testingA nonparametric two-sample test using a parametric integral probability metric
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 t…
Two-sample testingLOTTERY: Learning from Reference-Only Samples in Two-Sample Testing under Size Asymmetry
Data-adaptive two-sample testing assesses if two samples come from the same distribution, using a discrepancy learned from the data (e.g., via kernel-based feature representations). Such methods typically rely on data sp…
Two-sample testingCounterfactual Explanations for Deep Two-Sample Testing
Two-sample testing is a fundamental tool for detecting distributional differences across scientific domains, but classical tests (including kernel-based tests) can be ineffective on high-dimensional structured data such …
Two-sample testingA Martingale Kernel Independence Test
The Hilbert-Schmidt Independence Criterion (HSIC) and its joint-independence extension $d\mathrm{HSIC}$ are degenerate $V$-statistics whose data-dependent weighted-$χ^2$ null limits force a permutation calibration that m…
Two-sample testingDistance-Matrix Wasserstein Statistics for Scalable Gromov--Wasserstein Learning
Gromov--Wasserstein (GW) distances compare graphs, shapes, and point clouds through internal distances, without requiring a common coordinate system. This invariance is powerful, but discrete GW is a nonconvex quadratic …
Graph ClassificationTwo-sample testingPoint CloudsKernel Selection is Model Selection: A Unified Complexity-Penalized Approach for MMD Two-Sample Tests
The Maximum Mean Discrepancy (MMD) is a cornerstone statistic for nonparametric two-sample testing, but its test power is dictated entirely by the chosen kernel. Because any fixed kernel inherently fails to distinguish c…
Two-sample testingA Semi-Supervised Kernel Two-Sample Test
We consider the problem of two-sample testing in a semi-supervised setting with abundant unlabeled covariate data. Standard two-sample tests neglect covariate information, which has the potential to significantly boost p…
Two-sample testingData Fusion with Distributional Equivalence Test-then-pool
Randomized controlled trials (RCTs) are the gold standard for causal inference, yet practical constraints often limit the size of the concurrent control arm. Borrowing control data from previous trials offers a potential…
Two-sample testingCausal InferenceMaximum entropy based testing in network models: ERGMs and constrained optimization
Stochastic network models play a central role across a wide range of scientific disciplines, and questions of statistical inference arise naturally in this context. In this paper we investigate goodness-of-fit and two-sa…
Two-sample testingDistribution-free two-sample testing with blurred total variation distance
Two-sample testing, where we aim to determine whether two distributions are equal or not equal based on samples from each one, is challenging if we cannot place assumptions on the properties of the two distributions. In …
Two-sample testingRegularized $f$-Divergence Kernel Tests
We propose a framework to construct practical kernel-based two-sample tests from the family of $f$-divergences. The test statistic is computed from the witness function of a regularized variational representation of the …
Two-sample testingUnified Unbiased Variance Estimation for Maximum Mean Discrepancy: Robust Finite-Sample Performance with Imbalanced Data and Exact Acceleration under Null and Alternative Hypotheses
The maximum mean discrepancy (MMD) is a kernel-based nonparametric statistic for two-sample testing, whose inferential accuracy depends critically on variance characterization. Existing work provides various finite-sampl…
Two-sample testingMaximum Mean Discrepancy with Unequal Sample Sizes via Generalized U-Statistics
Existing two-sample testing techniques, particularly those based on choosing a kernel for the Maximum Mean Discrepancy (MMD), often assume equal sample sizes from the two distributions. Applying these methods in practice…
Two-sample testingRestricted Block Permutation for Two-Sample Testing
We study a structured permutation scheme for two-sample testing that restricts permutations to single cross-swaps between block-selected representatives. Our analysis yields three main results. First, we provide an exact…
Two-sample testingMinimax-Optimal Two-Sample Test with Sliced Wasserstein
We study the problem of nonparametric two-sample testing using the sliced Wasserstein (SW) distance. While prior theoretical and empirical work indicates that the SW distance offers a promising balance between strong sta…
Computational EfficiencyTwo-sample testingKernel Two-Sample Testing via Directional Components Analysis
Standard kernel two-sample tests, such as those based on the Maximum Mean Discrepancy (MMD), aggregate squared differences across all directions in a Reproducing Kernel Hilbert Space (RKHS). However, in finite samples, t…
Two-sample testingConformal C2ST: Turning weak classifiers into strong two-sample tests
The two-sample testing problem, a fundamental task in statistics and machine learning, seeks to determine whether two sets of samples, drawn from underlying distributions $p$ and $q$, are in fact identically distributed …
Two-sample testingBayesian InferenceConformal Data Contamination Tests for Trading or Sharing of Data
The amount of quality data in many machine learning tasks is limited to what is available locally to data owners. The set of quality data can be expanded through trading or sharing with external data agents. However, dat…
Two-sample testingOutlier Detection