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

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

Lars Henry Berge Olsen
I. Glad
Martin Jullum
K. Aas
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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
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
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
On the influence of dependent features in classification problems: a game-theoretic perspective
Laura Davila-Pena
Alejandro Saavedra-Nieves
Balbina Casas-Méndez
TDI
FAtt
13
0
0
05 Aug 2024
Efficient and Accurate Explanation Estimation with Distribution Compression
Efficient and Accurate Explanation Estimation with Distribution Compression
Hubert Baniecki
Giuseppe Casalicchio
Bernd Bischl
Przemyslaw Biecek
FAtt
44
3
0
26 Jun 2024
Approximation of group explainers with coalition structure using Monte Carlo sampling on the product space of coalitions and features
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
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