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
Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison—0
Visual Scene Representations: Contrast, Scaling and Occlusion—0
Wald-Kernel: Learning to Aggregate Information for Sequential Inference—0
Wavelet based multi-scale shape features on arbitrary surfaces for cortical thickness discrimination—0
Limits of Deepfake Detection: A Robust Estimation Viewpoint—0
Weighted Sampling for Combined Model Selection and Hyperparameter Tuning—0
Active Sequential Two-Sample Testing—0
Adaptive Active Hypothesis Testing under Limited Information—0
Adaptive Concentration Inequalities for Sequential Decision Problems—0
Adaptive learning of density ratios in RKHS—0
Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing—0
Weakly Supervised Instance Learning for Thyroid Malignancy Prediction from Whole Slide Cytopathology Images—0
Advanced Tutorial: Label-Efficient Two-Sample Tests—0
Adversarial learning for product recommendation—0
Adversarially Robust Classification based on GLRT—0
A Flexible Framework for Hypothesis Testing in High-dimensions—0
A framework for paired-sample hypothesis testing for high-dimensional data—0
A General Framework for Distributed Inference with Uncertain Models—0
A Mean-Field Theory for Kernel Alignment with Random Features in Generative and Discriminative Models—0
A More Powerful Two-Sample Test in High Dimensions using Random Projection—0
A New Approach for Distributed Hypothesis Testing with Extensions to Byzantine-Resilience—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
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing—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