SOTAVerified

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 251–275 of 338 papers

TitleStatusHype
Reasoning with Memory Augmented Neural Networks for Language Comprehension—0
Two-sample testing in non-sparse high-dimensional linear models—0
Learning in Implicit Generative Models—0
Linear Hypothesis Testing in Dense High-Dimensional Linear Models—0
Towards the Design of Prospect-Theory based Human Decision Rules for Hypothesis Testing—0
Statistical comparison of classifiers through Bayesian hierarchical modellingCode1
Information Theoretic Structure Learning with Confidence—0
Size-Consistent Statistics for Anomaly Detection in Dynamic Networks—0
A review of Gaussian Markov models for conditional independence—0
Kernel Mean Embedding of Distributions: A Review and Beyond—0
Efficient Nonparametric Smoothness EstimationCode0
Information Recovery in Shuffled Graphs via Graph Matching—0
Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing—0
A U-statistic Approach to Hypothesis Testing for Structure Discovery in Undirected Graphical ModelsCode0
Interpretability of Multivariate Brain Maps in Brain Decoding: Definition and QuantificationCode0
Online Rules for Control of False Discovery Rate and False Discovery Exceedance—0
Classical Statistics and Statistical Learning in Imaging Neuroscience—0
Selective Inference Approach for Statistically Sound Predictive Pattern Mining—0
Distributed Information-Theoretic Clustering—0
Toward Optimal Feature Selection in Naive Bayes for Text Categorization—0
Classification accuracy as a proxy for two sample testing—0
Minimax Lower Bounds for Linear Independence Testing—0
Proactive Message Passing on Memory Factor Networks—0
Sharp Computational-Statistical Phase Transitions via Oracle Computational Model—0
Unsupervised Feature Construction for Improving Data Representation and Semantics—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