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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 151–200 of 338 papers

TitleStatusHype
Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models—0
Semi-Autoregressive Energy Flows: Exploring Likelihood-Free Training of Normalizing Flows—0
Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model—0
Epistemology of Modeling and Simulation: How can we gain Knowledge from Simulations?—0
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
Extracting relations between outcomes and significance levels in Randomized Controlled Trials (RCTs) publications—0
Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing—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
General Framework for Binary Classification on Top Samples—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
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
Guaranteed Deterministic Bounds on the Total Variation Distance between Univariate Mixtures—0
How Many Machines Can We Use in Parallel Computing for Kernel Ridge Regression?—0
How Secure are Deep Learning Algorithms from Side-Channel based Reverse Engineering?—0
How to Control the Error Rates of Binary Classifiers—0
How to Formulate and Solve Statistical Recognition and Learning Problems—0
HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models—0
Hypothesis Testing based Intrinsic Evaluation of Word Embeddings—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
Image-derived generative modeling of pseudo-macromolecular structures - towards the statistical assessment of Electron CryoTomography template matching—0
Improved Differentially Private Analysis of Variance—0
Improved Sum-of-Squares Lower Bounds for Hidden Clique and Hidden Submatrix Problems—0
Information Recovery in Shuffled Graphs via Graph Matching—0
Information Theoretic Structure Learning with Confidence—0
Instance-Based Classification through Hypothesis Testing—0
Introduction to logistic regression—0
Necessary and Sufficient Conditions for Inverse Reinforcement Learning of Bayesian Stopping Time Problems—0
Iterative hypothesis testing for multi-object tracking in presence of features with variable reliability—0
Kernel Change-point Analysis—0
Kernel Mean Embedding Based Hypothesis Tests for Comparing Spatial Point Patterns—0
Kernel Mean Embedding of Distributions: A Review and Beyond—0
Kernel Two-Sample Hypothesis Testing Using Kernel Set Classification—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