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 201–250 of 338 papers

TitleStatusHype
Speeding up Permutation Testing in Neuroimaging—0
Statistical Agnostic Mapping: a Framework in Neuroimaging based on Concentration Inequalities—0
Statistical Analysis based Hypothesis Testing Method in Biological Knowledge Discovery—0
Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances—0
Statistical Query Algorithms and Low-Degree Tests Are Almost Equivalent—0
Statistical Testing on ASR Performance via Blockwise Bootstrap—0
Statistical Topological Data Analysis - A Kernel Perspective—0
Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain—0
Stock Price Forecasting and Hypothesis Testing Using Neural Networks—0
Stopping criterion for active learning based on deterministic generalization bounds—0
Strictly Proper Kernel Scoring Rules and Divergences with an Application to Kernel Two-Sample Hypothesis Testing—0
Surprise: Result List Truncation via Extreme Value Theory—0
Team Harry Friberg at SemEval-2019 Task 4: Identifying Hyperpartisan News through Editorially Defined Metatopics—0
Testing and Learning on Distributions with Symmetric Noise Invariance—0
Testing Changes in Communities for the Stochastic Block Model—0
Testing correlation of unlabeled random graphs—0
Testing for Families of Distributions via the Fourier Transform—0
Testing Hypotheses by Regularized Maximum Mean Discrepancy—0
Testing Identity of Multidimensional Histograms—0
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference—0
The Exact Equivalence of Distance and Kernel Methods for Hypothesis Testing—0
Universally Consistent K-Sample Tests via Dependence Measures—0
The Fundamental Learning Problem that Genetic Algorithms with Uniform Crossover Solve Efficiently and Repeatedly As Evolution Proceeds—0
The Lasso with general Gaussian designs with applications to hypothesis testing—0
The Perturbed Variation—0
The p-filter: multi-layer FDR control for grouped hypotheses—0
The Power of Batching in Multiple Hypothesis Testing—0
The power of synergy in differential privacy: Combining a small curator with local randomizers—0
The Role of Interactivity in Local Differential Privacy—0
The Structure of Optimal Private Tests for Simple Hypotheses—0
Veridical Data Science—0
Tight Regret Bounds for Noisy Optimization of a Brownian Motion—0
Toward Optimal Feature Selection in Naive Bayes for Text Categorization—0
Toward Optimal Run Racing: Application to Deep Learning Calibration—0
Towards an Evolvable Cancer Treatment Simulator—0
Towards Integration of Statistical Hypothesis Tests into Deep Neural Networks—0
Towards the Design of Prospect-Theory based Human Decision Rules for Hypothesis Testing—0
Training Neural Networks for Likelihood/Density Ratio Estimation—0
Two-sample Hypothesis Testing for Inhomogeneous Random Graphs—0
Two-sample testing in non-sparse high-dimensional linear models—0
Two-Sample Testing on Ranked Preference Data and the Role of Modeling Assumptions—0
Two-Sample Tests for Large Random Graphs Using Network Statistics—0
Unbiased estimators for the variance of MMD estimators—0
Understanding Classifier Mistakes with Generative Models—0
Understanding Classifiers with Generative Models—0
Universal Hypothesis Testing with Kernels: Asymptotically Optimal Tests for Goodness of Fit—0
Universality of Computational Lower Bounds for Submatrix Detection—0
Unsupervised Feature Construction for Improving Data Representation and Semantics—0
Unsupervised Textual Grounding: Linking Words to Image Concepts—0
Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison—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