A collection of research materials on explainable AI/ML

This is an implementation of an integer programming approach to cluster description that works by generating polyhedra around each cluster.

This package contains the data and code for running ORL experiments, associated with the paper Learning Customized and Optimized Lists of Rules with Mathematical Programming by Cynthia Rudin and Se…

Generate Diverse Counterfactual Explanations for any machine learning model.

Bayesian or-of-and

This repository contains the source code of the paper "Learning Accurate and Interpretable Decision Rule Sets from Neural Networks".

Matlab implementation of ROCK(RObust Clustering using linKs) clustering algorithm

A library of extension and helper modules for Python's data analysis and machine learning libraries.

An implementation of IDS (Interpretable Decision Sets) algorithm.

Source code for paper Zhang, Guangyi, and Aristides Gionis. "Diverse Rule Sets." in KDD 2020

⏰ AI conference deadline countdowns

Shallow decision trees for explainable clustering

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

R package which provides functions to fit IRT models for multiple choice items.

Expanding Explainable K-Means Clustering

Installation and implementation guidelines of ICOT, a Julia-based interpretable clustering algorithm.

This folder contains the ESSC method for Community Extraction in R

Code and data for the paper "A Combinatorial Multi-Armed Bandit Approach to Correlation Clustering", DAMI 2023

Code and experimental results for LambdaCC

Uniform Manifold Approximation and Projection - R package

An R package implementing the UMAP dimensionality reduction method.

R package implementing the density-preserving data visualization tools den-SNE and densMAP

Density-preserving data visualization tools den-SNE and densMAP

Sparse Principal Component Analysis (SPCA) using Variable Projection

An interactive graphing library for R

ggplot2 extension for alluvial plots

A novel Clustering algorithm by measuring Direction Centrality (CDC) locally. It adopts a density-independent metric based on the distribution of K-nearest neighbors (KNNs) to distinguish between i…

Library for python community to find the similarity or distance between two entities containing categorical data

A library of sklearn compatible categorical variable encoders