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 51–75 of 338 papers

TitleStatusHype
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing—0
Can A User Anticipate What Her Followers Want?—0
Classical Statistics and Statistical Learning in Imaging Neuroscience—0
Classification accuracy as a proxy for two sample testing—0
A Flexible Framework for Hypothesis Testing in High-dimensions—0
CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks—0
Closing the AI Knowledge Gap—0
Collaborative non-parametric two-sample testing—0
Communication and Memory Efficient Testing of Discrete Distributions—0
A framework for paired-sample hypothesis testing for high-dimensional data—0
A powerful and efficient set test for genetic markers that handles confounders—0
Efficient Benchmarking of NLP APIs using Multi-armed Bandits—0
Dealing with Uncertainties in User Feedback: Strategies Between Denying and Accepting—0
Contextual Online False Discovery Rate Control—0
Adaptive Active Hypothesis Testing under Limited Information—0
Deciphering Dynamical Nonlinearities in Short Time Series Using Recurrent Neural Networks—0
Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models—0
A Simple Way to Deal with Cherry-picking—0
Confidence Intervals and Hypothesis Testing for High-Dimensional Regression—0
Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop—0
Kernel Hypothesis Testing with Set-valued Data—0
Weakly Supervised Instance Learning for Thyroid Malignancy Prediction from Whole Slide Cytopathology Images—0
A Sparse Linear Model and Significance Test for Individual Consumption Prediction—0
Counterexamples to the Low-Degree Conjecture—0
A General Framework for Distributed Inference with Uncertain Models—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