InformaticsStudentsAnalysis is an analysis project utilizing two methods: the 'One Sample Test' and the 'Two Sample Test' to verify the validity of a statement. This project was completed as part of my coursework for the 4th semester at IMT@UC.

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.

Code for the paper "Two-Sample Test with Copula Entropy"

InformaticsStudentsDataset is a dataset containing dummy data of all informatics students from Institut Teknologi Sepuluh Nopember (ITS). This dataset is used for case analysis of 'One Sample Test' and 'Two Sample Test' in the repository (https://github.com/LouisFernando1204/InformaticsStudentsAnalysis).

Code for the paper "An Empirical Analysis of KDE-based Generative Models on Small Datasets"

R package - Implementation of high-probability lower bounds for the total variance distance (Michel et al., 2020)

Robust and Highly Sensitive Covariate Shift Detection using XGBoost

TATTER (Two-sAmple TesT EstimatoR) is a tool to perform two-sample hypothesis test.

Code for our TMLR '24 Journal: MMD-Regularized UOT.

Code for the paper "Change Point Detection with Copula Entropy based Two-Sample Test"

Official repository for the ICLR 2023 paper "A Learning Based Hypothesis Test for Harmful Covariate Shift"

A Python package implementing a variety of statistical methods that rely on kernels (e.g. HSIC for independence testing).

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