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Two-sample testing

In statistical hypothesis testing, a two-sample test is a test performed on the data of two random samples, each independently obtained from a different given population. The purpose of the test is to determine whether the difference between these two populations is statistically significant. The statistics used in two-sample tests can be used to solve many machine learning problems, such as domain adaptation, covariate shift and generative adversarial networks.

Papers

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Showing 101–150 of 338 papers

TitleStatusHype
Equitability, interval estimation, and statistical power—0
Equivalence of distance-based and RKHS-based statistics in hypothesis testing—0
Exact Post Model Selection Inference for Marginal Screening—0
A General Framework for Distributed Inference with Uncertain Models—0
Extracting relations between outcomes and significance levels in Randomized Controlled Trials (RCTs) publications—0
Can A User Anticipate What Her Followers Want?—0
Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing—0
Classical Statistics and Statistical Learning in Imaging Neuroscience—0
From Shannon's Channel to Semantic Channel via New Bayes' Formulas for Machine Learning—0
Smooth p-Wasserstein Distance: Structure, Empirical Approximation, and Statistical Applications—0
From Two Sample Testing to Singular Gaussian Discrimination—0
Fundamental Limits of Testing the Independence of Irrelevant Alternatives in Discrete Choice—0
CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks—0
General Framework for Binary Classification on Top Samples—0
Closing the AI Knowledge Gap—0
Generalization Error Bounds via mth Central Moments of the Information Density—0
Generalized Binary Search For Split-Neighborly Problems—0
Generalized Multivariate Signs for Nonparametric Hypothesis Testing in High Dimensions—0
Generalized Sliced Distances for Probability Distributions—0
Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics—0
A framework for paired-sample hypothesis testing for high-dimensional data—0
Geometric Inference for General High-Dimensional Linear Inverse Problems—0
Geometric tree kernels: Classification of COPD from airway tree geometry—0
Goodness-of-Fit Tests for Inhomogeneous Random Graphs—0
Measuring Sample Quality with Kernels—0
Guaranteed Deterministic Bounds on the Total Variation Distance between Univariate Mixtures—0
Hypothesis Testing based Intrinsic Evaluation of Word Embeddings—0
HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models—0
Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing—0
Introduction to logistic regression—0
How to Formulate and Solve Statistical Recognition and Learning Problems—0
How to Control the Error Rates of Binary Classifiers—0
Hypothesis Testing for Automated Community Detection in Networks—0
Hypothesis Testing For Densities and High-Dimensional Multinomials: Sharp Local Minimax Rates—0
Hypothesis Testing for High-Dimensional Multinomials: A Selective Review—0
Hypothesis Testing in Feedforward Networks with Broadcast Failures—0
Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory—0
Hypothesis Testing Interpretations and Renyi Differential Privacy—0
Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease—0
Identification of Model Uncertainty via Optimal Design of Experiments Applied to a Mechanical Press—0
Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test—0
Improved Differentially Private Analysis of Variance—0
How Secure are Deep Learning Algorithms from Side-Channel based Reverse Engineering?—0
Information Recovery in Shuffled Graphs via Graph Matching—0
Information Theoretic Structure Learning with Confidence—0
Instance-Based Classification through Hypothesis Testing—0
A Sparse Linear Model and Significance Test for Individual Consumption Prediction—0
Kernel Change-point Analysis—0
Counterexamples to the Low-Degree Conjecture—0
How Many Machines Can We Use in Parallel Computing for Kernel Ridge Regression?—0
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All datasetsBlob (9 modes, 40 for each)CIFAR-10 vs CIFAR-10.1 (1000 samples)HDGM (d=10, N=4000)HIGGS Data SetMNIST vs Fake MNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy98.5—Unverified
#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy74.4—Unverified
#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy65.9—Unverified
#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy57.9—Unverified
#ModelMetricClaimedVerifiedStatus
1MMD-DAvg accuracy91—Unverified