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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 101–150 of 338 papers

TitleStatusHype
Decision-Making with Auto-Encoding Variational BayesCode1
HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models—0
Limits of Detecting Text Generated by Large-Scale Language Models—0
Two-Sample Testing for Event Impacts in Time SeriesCode0
Modelling and Quantifying Membership Information Leakage in Machine Learning—0
Tight Regret Bounds for Noisy Optimization of a Brownian Motion—0
Fundamental Limits of Testing the Independence of Irrelevant Alternatives in Discrete Choice—0
Breaking hypothesis testing for failure ratesCode0
Goodness-of-Fit Tests for Inhomogeneous Random Graphs—0
Copy Move Source-Target Disambiguation through Multi-Branch CNNsCode0
Approval policies for modifications to Machine Learning-Based Software as a Medical Device: A study of bio-creepCode0
Statistical Agnostic Mapping: a Framework in Neuroimaging based on Concentration Inequalities—0
Event Outlier Detection in Continuous TimeCode0
Statistical Testing on ASR Performance via Blockwise Bootstrap—0
The power of synergy in differential privacy: Combining a small curator with local randomizers—0
Comparing distributions: _1 geometry improves kernel two-sample testingCode0
Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models—0
Line-based Camera Pose Estimation in Point Cloud of Structured Environments—0
Minimax Nonparametric Two-sample Test under Smoothing—0
Training Neural Networks for Likelihood/Density Ratio Estimation—0
Noiseless Privacy—0
Sequential Controlled Sensing for Composite Multihypothesis Testing—0
Universally Consistent K-Sample Tests via Dependence Measures—0
Identification of Model Uncertainty via Optimal Design of Experiments Applied to a Mechanical Press—0
Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing—0
Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics—0
Two-sample Testing Using Deep LearningCode0
The Power of Batching in Multiple Hypothesis Testing—0
Ctrl-Z: Recovering from Instability in Reinforcement Learning—0
A Test for Shared Patterns in Cross-modal Brain Activation AnalysisCode0
A New Framework for Distance and Kernel-based Metrics in High Dimensions—0
Nonzero-sum Adversarial Hypothesis Testing GamesCode0
Classification Logit Two-sample Testing by Neural NetworksCode0
A Mean-Field Theory for Kernel Alignment with Random Features in Generative and Discriminative Models—0
Comparing distributions: _1 geometry improves kernel two-sample testingCode0
Weighted Sampling for Combined Model Selection and Hyperparameter Tuning—0
Distance Assessment and Hypothesis Testing of High-Dimensional Samples using Variational Autoencoders—0
Can A User Anticipate What Her Followers Want?—0
Stock Price Forecasting and Hypothesis Testing Using Neural Networks—0
Asymptotically Optimal One- and Two-Sample Testing with Kernels—0
Minimum Description Length Revisited—0
Extracting relations between outcomes and significance levels in Randomized Controlled Trials (RCTs) publications—0
Measuring Gender Bias in Word Embeddings across Domains and Discovering New Gender Bias Word CategoriesCode0
Notes on Computational Hardness of Hypothesis Testing: Predictions using the Low-Degree Likelihood Ratio—0
Deciphering Dynamical Nonlinearities in Short Time Series Using Recurrent Neural Networks—0
Kernel Hypothesis Testing with Set-valued Data—0
Conditional Independence Testing using Generative Adversarial NetworksCode0
A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine-Resilience—0
hyppo: A Multivariate Hypothesis Testing Python PackageCode0
Bayes Test of Precision, Recall, and F1 Measure for Comparison of Two Natural Language Processing 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