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 151–175 of 338 papers

TitleStatusHype
hyppo: A Multivariate Hypothesis Testing Python PackageCode0
Bayes Test of Precision, Recall, and F1 Measure for Comparison of Two Natural Language Processing Models—0
On the Self-Similarity of Natural Stochastic Textures—0
Towards Integration of Statistical Hypothesis Tests into Deep Neural Networks—0
Early Detection of Long Term Evaluation Criteria in Online Controlled Experiments—0
Communication and Memory Efficient Testing of Discrete Distributions—0
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmCode0
Unbiased estimators for the variance of MMD estimators—0
Measuring and Modeling Language Change—0
Team Harry Friberg at SemEval-2019 Task 4: Identifying Hyperpartisan News through Editorially Defined Metatopics—0
Kernel Mean Embedding Based Hypothesis Tests for Comparing Spatial Point Patterns—0
Hypothesis Testing Interpretations and Renyi Differential Privacy—0
Revisiting Precision and Recall Definition for Generative Model EvaluationCode0
Limits of Deepfake Detection: A Robust Estimation Viewpoint—0
Gaussian Differential PrivacyCode0
Weakly Supervised Instance Learning for Thyroid Malignancy Prediction from Whole Slide Cytopathology Images—0
CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks—0
Scalable and Efficient Hypothesis Testing with Random ForestsCode0
The Role of Interactivity in Local Differential Privacy—0
Interpreting Black Box Models via Hypothesis TestingCode0
A New Approach for Distributed Hypothesis Testing with Extensions to Byzantine-Resilience—0
Improved Differentially Private Analysis of Variance—0
Universality of Computational Lower Bounds for Submatrix Detection—0
Contextual Online False Discovery Rate Control—0
Optimal Nonparametric Inference via Deep Neural Network—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