TimeSHAP explains Recurrent Neural Network predictions.

Fast approximate Shapley values in R

Paper collection of federated learning. Conferences and Journals Collection for Federated Learning from 2019 to 2021, Accepted Papers, Hot topics and good research groups. Paper summary

An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model

Counterfactual SHAP: a framework for counterfactual feature importance

Source code for the Joint Shapley values: a measure of joint feature importance

Weighted Shapley Values and Weighted Confidence Intervals for Multiple Machine Learning Models and Stacked Ensembles

In this paper we researched the accuracy and usability of machine learning models for MMM analyses.

Counterfactual Shapley Additive Explanation: Experiments

Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation (ACL 2020)

[IJCAI 2024] Redefining Contributions: Shapley-Driven Federated Learning

Experimental toolbox for quantum Shapley values.

Reference implementation of the paper Unsupervised Features Ranking via Coalitional Game Theory for Categorical Data

This repository is the official implementation of Explainable Prediction of Acute Myocardial Infarction using Machine Learning and Shapley Values published in IEEE Access in November 2020.

HERALD: An Annotation Efficient Method to Train User Engagement Predictors in Dialogs (ACL 2021)

A radiomic interpretation tool based on Shapley values

Slides for the "Interpretable SDM with Julia" workshop

Shapley-based decomposition to anatomize the of out-of-sample accuracy of time-series forecasting models

Code and experiments related to SHAPEffects paper: 'A feature selection method based on Shapley values robust to concept shift in regression'

Android malware detection using machine learning.