TATTER (Two-sAmple TesT EstimatoR) is a tool to perform two-sample hypothesis test.
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Updated
Feb 15, 2022 - Python
TATTER (Two-sAmple TesT EstimatoR) is a tool to perform two-sample hypothesis test.
Code for the paper "Change Point Detection with Copula Entropy based Two-Sample Test"
Code for the paper "An Empirical Analysis of KDE-based Generative Models on Small Datasets"
Analysis of A/B test results by an e-commerce company. Application of hypothesis testing to determining whether the company should implement the new web page it developed to increase users' conversion rate.
Robust and Highly Sensitive Covariate Shift Detection using XGBoost
Code for the paper "Two-Sample Test with Copula Entropy"
Code for our TMLR '24 Journal: MMD-Regularized UOT.
R package - Implementation of high-probability lower bounds for the total variance distance (Michel et al., 2020)
A Python package implementing a variety of statistical methods that rely on kernels (e.g. HSIC for independence testing).
Official repository for the ICLR 2023 paper "A Learning Based Hypothesis Test for Harmful Covariate Shift"
R package for estimating copula entropy (mutual information), transfer entropy (conditional mutual information), and the statistic for multivariate normality test and two-sample test
This is the source code for Learning Deep Kernels for Non-Parametric Two-Sample Tests (ICML2020).
Estimating Copula Entropy (Mutual Information), Transfer Entropy (Conditional Mutual Information), and the statistics for multivariate normality test and two-sample test, and change point detection in Python
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