Papers Two-sample testing
“Two-sample testing” 태그가 달린 논문 360편 · 필터 해제
Two-cluster test
Cluster analysis is a fundamental research issue in statistics and machine learning. In many modern clustering methods, we need to determine whether two subsets of samples come from the same cluster. Since these subsets …
Two-sample testingKernel Trace Distance: Quantum Statistical Metric between Measures through RKHS Density Operators
Distances between probability distributions are a key component of many statistical machine learning tasks, from two-sample testing to generative modeling, among others. We introduce a novel distance between measures tha…
Two-sample testingLeveraging Optimal Transport for Distributed Two-Sample Testing: An Integrated Transportation Distance-based Framework
This paper introduces a novel framework for distributed two-sample testing using the Integrated Transportation Distance (ITD), an extension of the Optimal Transport distance. The approach addresses the challenges of dete…
Federated LearningTwo-sample testingSignature Maximum Mean Discrepancy Two-Sample Statistical Tests
Maximum Mean Discrepancy (MMD) is a widely used concept in machine learning research which has gained popularity in recent years as a highly effective tool for comparing (finite-dimensional) distributions. Since it is de…
Two-sample testingFrom Two Sample Testing to Singular Gaussian Discrimination
We establish that testing for the equality of two probability measures on a general separable and compact metric space is equivalent to testing for the singularity between two corresponding Gaussian measures on a suitabl…
Two-sample testingAdvanced Tutorial: Label-Efficient Two-Sample Tests
Hypothesis testing is a statistical inference approach used to determine whether data supports a specific hypothesis. An important type is the two-sample test, which evaluates whether two sets of data points are from ide…
Active LearningTwo-sample testingOptimal Algorithms for Augmented Testing of Discrete Distributions
We consider the problem of hypothesis testing for discrete distributions. In the standard model, where we have sample access to an underlying distribution $p$, extensive research has established optimal bounds for unifor…
Two-sample testingA Unified Data Representation Learning for Non-parametric Two-sample Testing
Learning effective data representations has been crucial in non-parametric two-sample testing. Common approaches will first split data into training and test sets and then learn data representations purely on the trainin…
Representation LearningTwo-sample testingMinimax Optimal Two-Sample Testing under Local Differential Privacy
We explore the trade-off between privacy and statistical utility in private two-sample testing under local differential privacy (LDP) for both multinomial and continuous data. We begin by addressing the multinomial case,…
Two-sample testingModel Equality Testing: Which Model Is This API Serving?
Users often interact with large language models through black-box inference APIs, both for closed- and open-weight models (e.g., Llama models are popularly accessed via Amazon Bedrock and Azure AI Studio). In order to cu…
modelTwo-sample testingGeneral Frameworks for Conditional Two-Sample Testing
We study the problem of conditional two-sample testing, which aims to determine whether two populations have the same distribution after accounting for confounding factors. This problem commonly arises in various applica…
Domain AdaptationFairnessTwo-sample testingvalidCredal Two-Sample Tests of Epistemic Uncertainty
We introduce credal two-sample testing, a new hypothesis testing framework for comparing credal sets -- convex sets of probability measures where each element captures aleatoric uncertainty and the set itself represents …
Two-sample testingMachine Learning for Two-Sample Testing under Right-Censored Data: A Simulation Study
The focus of this study is to evaluate the effectiveness of Machine Learning (ML) methods for two-sample testing with right-censored observations. To achieve this, we develop several ML-based methods with varying archite…
Two-sample testingComputational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier Features
Recent years have seen a surge in methods for two-sample testing, among which the Maximum Mean Discrepancy (MMD) test has emerged as an effective tool for handling complex and high-dimensional data. Despite its success a…
Two-sample testingNetwork two-sample test for block models
We consider the two-sample testing problem for networks, where the goal is to determine whether two sets of networks originated from the same stochastic model. Assuming no vertex correspondence and allowing for different…
Graph MatchingOverall - TestStochastic Block ModelTwo-sample testingStatistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances
Optimal transport has been very successful for various machine learning tasks; however, it is known to suffer from the curse of dimensionality. Hence, dimensionality reduction is desirable when applied to high-dimensiona…
Dimensionality ReductionTwo-sample testingCollaborative non-parametric two-sample testing
This paper addresses the multiple two-sample test problem in a graph-structured setting, which is a common scenario in fields such as Spatial Statistics and Neuroscience. Each node $v$ in fixed graph deals with a two-sam…
Two-sample testingVariable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison
Two-sample testing decides whether two datasets are generated from the same distribution. This paper studies variable selection for two-sample testing, the task being to identify the variables (or dimensions) responsible…
Causal InferenceRelevance DetectionTwo-sample testingVariable SelectionDeep anytime-valid hypothesis testing
We propose a general framework for constructing powerful, sequential hypothesis tests for a large class of nonparametric testing problems. The null hypothesis for these problems is defined in an abstract form using the a…
Adversarial RobustnessTwo-sample testingvalidA framework for paired-sample hypothesis testing for high-dimensional data
The standard paired-sample testing approach in the multidimensional setting applies multiple univariate tests on the individual features, followed by p-value adjustments. Such an approach suffers when the data carry nume…
Two-sample testing