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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–200 of 338 papers

TitleStatusHype
Safe TestingCode1
On the Self-Similarity of Natural Stochastic Textures—0
Towards Integration of Statistical Hypothesis Tests into Deep Neural Networks—0
Early Detection of Long Term Evaluation Criteria in Online Controlled Experiments—0
Communication and Memory Efficient Testing of Discrete Distributions—0
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmCode0
Unbiased estimators for the variance of MMD estimators—0
Measuring and Modeling Language Change—0
Team Harry Friberg at SemEval-2019 Task 4: Identifying Hyperpartisan News through Editorially Defined Metatopics—0
Kernel Mean Embedding Based Hypothesis Tests for Comparing Spatial Point Patterns—0
Hypothesis Testing Interpretations and Renyi Differential Privacy—0
Revisiting Precision and Recall Definition for Generative Model EvaluationCode0
Limits of Deepfake Detection: A Robust Estimation Viewpoint—0
Gaussian Differential PrivacyCode0
Weakly Supervised Instance Learning for Thyroid Malignancy Prediction from Whole Slide Cytopathology Images—0
CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks—0
Scalable and Efficient Hypothesis Testing with Random ForestsCode0
The Role of Interactivity in Local Differential Privacy—0
Interpreting Black Box Models via Hypothesis TestingCode0
A New Approach for Distributed Hypothesis Testing with Extensions to Byzantine-Resilience—0
Improved Differentially Private Analysis of Variance—0
Universality of Computational Lower Bounds for Submatrix Detection—0
Contextual Online False Discovery Rate Control—0
Optimal Nonparametric Inference via Deep Neural Network—0
Local minimax rates for closeness testing of discrete distributions—0
Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test—0
Veridical Data Science—0
Instance-Based Classification through Hypothesis Testing—0
Towards an Evolvable Cancer Treatment Simulator—0
Adversarial Sample Detection for Deep Neural Network through Model Mutation TestingCode0
Sequential Experiment Design for Hypothesis Verification—0
Testing for Families of Distributions via the Fourier Transform—0
Practical methods for graph two-sample testingCode0
Testing Changes in Communities for the Stochastic Block Model—0
The Structure of Optimal Private Tests for Simple Hypotheses—0
Variational Autoencoders for New Physics Mining at the Large Hadron ColliderCode0
Understanding Learned Models by Identifying Important Features at the Right ResolutionCode0
Minimax Rates in Network Analysis: Graphon Estimation, Community Detection and Hypothesis Testing—0
How Secure are Deep Learning Algorithms from Side-Channel based Reverse Engineering?—0
A tutorial on MDL hypothesis testing for graph analysis—0
Failing Loudly: An Empirical Study of Methods for Detecting Dataset ShiftCode0
Policy Design for Active Sequential Hypothesis Testing using Deep Learning—0
A Simple Way to Deal with Cherry-picking—0
Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop—0
Cross-situational learning of large lexicons with finite memory—0
Distributed Chernoff Test: Optimal decision systems over networks—0
Multi-level hypothesis testing for populations of heterogeneous networks—0
A Structured Review of the Validity of BLEU—0
Statistical Windows in Testing for the Initial Distribution of a Reversible Markov Chain—0
A Differentially Private Kernel Two-Sample TestCode0
Show:102550
← PrevPage 4 of 7Next →
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