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 51–100 of 338 papers

TitleStatusHype
Measuring Gender Bias in Word Embeddings across Domains and Discovering New Gender Bias Word CategoriesCode0
Adversarial Sample Detection for Deep Neural Network through Model Mutation TestingCode0
Kernel-Based Tests for Likelihood-Free Hypothesis TestingCode0
Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and GraphsCode0
Classification Logit Two-sample Testing by Neural NetworksCode0
A Witness Two-Sample TestCode0
Kernel Conditional Moment Test via Maximum Moment RestrictionCode0
Revisiting Precision and Recall Definition for Generative Model EvaluationCode0
Meta Two-Sample Testing: Learning Kernels for Testing with Limited DataCode0
Comparing distributions: _1 geometry improves kernel two-sample testingCode0
Comparing distributions: _1 geometry improves kernel two-sample testingCode0
Compress Then Test: Powerful Kernel Testing in Near-linear TimeCode0
Graphon based Clustering and Testing of Networks: Algorithms and TheoryCode0
Gaussian Differential PrivacyCode0
The hypergeometric test performs comparably to TF-IDF on standard text analysis tasksCode0
General Frameworks for Conditional Two-Sample TestingCode0
Interpretability of Multivariate Brain Maps in Brain Decoding: Definition and QuantificationCode0
Conditional Independence Testing using Generative Adversarial NetworksCode0
Generative Moment Matching NetworksCode0
Approval policies for modifications to Machine Learning-Based Software as a Medical Device: A study of bio-creepCode0
Failing Loudly: An Empirical Study of Methods for Detecting Dataset ShiftCode0
Event Outlier Detection in Continuous TimeCode0
Copy Move Source-Target Disambiguation through Multi-Branch CNNsCode0
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmCode0
A Differentially Private Kernel Two-Sample TestCode0
Credal Two-Sample Tests of Epistemic UncertaintyCode0
Achieving Equalized Odds by Resampling Sensitive AttributesCode0
Computational-Statistical Trade-off in Kernel Two-Sample Testing with Random Fourier FeaturesCode0
Data-adaptive statistics for multiple hypothesis testing in high-dimensional settingsCode0
A Meta-Analysis of the Anomaly Detection ProblemCode0
Fast Two-Sample Testing with Analytic Representations of Probability MeasuresCode0
Interpreting Black Box Models via Hypothesis TestingCode0
hyppo: A Multivariate Hypothesis Testing Python PackageCode0
A Test for Shared Patterns in Cross-modal Brain Activation AnalysisCode0
A powerful and efficient set test for genetic markers that handles confounders—0
Communication and Memory Efficient Testing of Discrete Distributions—0
A framework for paired-sample hypothesis testing for high-dimensional data—0
Collaborative non-parametric two-sample testing—0
Closing the AI Knowledge Gap—0
A novel family of non-parametric cumulative based divergences for point processes—0
CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks—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
Classification accuracy as a proxy for two sample testing—0
Classical Statistics and Statistical Learning in Imaging Neuroscience—0
Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning—0
Can A User Anticipate What Her Followers Want?—0
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing—0
Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model—0
Bottleneck Problems: Information and Estimation-Theoretic View—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