Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features

26 November 2021

Papers citing "Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features"

6 / 6 papers shown

Title
A Comprehensive Study of Shapley Value in Data Analytics Hong Lin Shixin Wan Zhongle Xie Ke Chen Meihui Zhang Lidan Shou Gang Chen 95 0 0 02 Dec 2024
Improving the Weighting Strategy in KernelSHAP Lars Henry Berge Olsen Martin Jullum TDI FAtt 71 2 0 07 Oct 2024
On the influence of dependent features in classification problems: a game-theoretic perspective Laura Davila-Pena Alejandro Saavedra-Nieves Balbina Casas-Méndez TDI FAtt 15 0 0 05 Aug 2024
Efficient and Accurate Explanation Estimation with Distribution Compression Hubert Baniecki Giuseppe Casalicchio Bernd Bischl Przemyslaw Biecek FAtt 46 3 0 26 Jun 2024
Approximation of group explainers with coalition structure using Monte Carlo sampling on the product space of coalitions and features Konstandinos Kotsiopoulos A. Miroshnikov Khashayar Filom Arjun Ravi Kannan FAtt 23 3 0 17 Mar 2023
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R Marvin N. Wright A. Ziegler 93 2,731 0 18 Aug 2015