A package for Displaying and Computing Benchmarking Results of Algorithmic Recourse and Counterfactual Explanation Algorithms
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Updated
Jul 31, 2024 - Python
A package for Displaying and Computing Benchmarking Results of Algorithmic Recourse and Counterfactual Explanation Algorithms
CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox
A package for Counterfactual Explanations and Algorithmic Recourse in Julia.
Pytorch implementation of 'Explaining text classifiers with counterfactual representations' (Lemberger & Saillenfest, 2024)
Survival-Patterns-based counterfactual explanations of survival models
CELS: Counterfactual Explanation for Time Series Data via Learned Saliency Maps (2023 Big data)
Optimal binning: monotonic binning with constraints. Support batch & stream optimal binning. Scorecard modelling and counterfactual explanations.
Local Universal Rule-based Explanations
A collection of research materials on explainable AI/ML
A Multi-Criteria Approach for Selecting an Explanation From the Set of Counterfactuals Produced by an Ensemble of Explainers
Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"
This repository is dedicated to PhD Research (Human-centered XAI)
Generate Diverse Counterfactual Explanations for any machine learning model.
An Open-Source Library for the interpretability of time series classifiers
Code for "Robust counterfactual explanations for random forests"
Counterfactual explanations for the identification of the features with the highest relevance on the shape of response curves generated by neural network black boxes
Code and data for decision making under strategic behavior, NeurIPS 2020 & Management Science 2024.
SG-CF Shapelet-Guided Counterfactual Explanation for Time Series Data (2022 Big Data)
Motif-guided time series counterfactual explanations (ICPR 2022)
Code for Master's thesis in Applied Physics and Mathematics at NTNU.
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