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 251–300 of 338 papers

TitleStatusHype
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
Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning—0
A novel family of non-parametric cumulative based divergences for point processes—0
A powerful and efficient set test for genetic markers that handles confounders—0
Estimating Linear Mixed Effects Models with Truncated Normally Distributed Random Effects—0
Kernel Hypothesis Testing with Set-valued Data—0
A Simple Way to Deal with Cherry-picking—0
A Sparse Linear Model and Significance Test for Individual Consumption Prediction—0
A strong converse bound for multiple hypothesis testing, with applications to high-dimensional estimation—0
A Structured Review of the Validity of BLEU—0
Asymptotically Optimal One- and Two-Sample Testing with Kernels—0
Asymptotic Analysis of Sampling Estimators for Randomized Numerical Linear Algebra Algorithms—0
A tutorial on MDL hypothesis testing for graph analysis—0
Bayesian Hypothesis Testing for Block Sparse Signal Recovery—0
Bayesian hypothesis testing for one bit compressed sensing with sensing matrix perturbation—0
Bayes Test of Precision, Recall, and F1 Measure for Comparison of Two Natural Language Processing Models—0
Bootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity—0
Bottleneck Problems: Information and Estimation-Theoretic View—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
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
Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test—0
Concept Drift Detection and Adaptation with Hierarchical 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