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 1–25 of 338 papers

TitleStatusHype
Testing Goodness of Fit of Conditional Density Models with KernelsCode1
AutoML Two-Sample TestCode1
Learning Opinion Dynamics From Social TracesCode1
Addressing Maximization Bias in Reinforcement Learning with Two-Sample TestingCode1
MMD Aggregated Two-Sample TestCode1
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference SettingCode1
Decision-Making with Auto-Encoding Variational BayesCode1
Learning Deep Kernels for Non-Parametric Two-Sample TestsCode1
Safe TestingCode1
Towards Probabilistic Verification of Machine UnlearningCode1
Statistical comparison of classifiers through Bayesian hierarchical modellingCode1
Model Equality Testing: Which Model Is This API Serving?Code1
Expert-Supervised Reinforcement Learning for Offline Policy Learning and EvaluationCode1
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularityCode1
A framework for paired-sample hypothesis testing for high-dimensional data—0
A Flexible Framework for Hypothesis Testing in High-dimensions—0
Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing—0
Adaptive learning of density ratios in RKHS—0
Adversarially Robust Classification based on GLRT—0
Active Sequential Two-Sample Testing—0
A powerful and efficient set test for genetic markers that handles confounders—0
A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine-Resilience—0
A New Framework for Distance and Kernel-based Metrics in High Dimensions—0
An explainable deep vision system for animal classification and detection in trail-camera images with automatic post-deployment retraining—0
A New Approach for Distributed Hypothesis Testing with Extensions to Byzantine-Resilience—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