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Manifold Restricted Interventional Shapley Values

Manifold Restricted Interventional Shapley Values

10 January 2023
Muhammad Faaiz Taufiq
Patrick Blobaum
Lenon Minorics
    FAtt
ArXivPDFHTML

Papers citing "Manifold Restricted Interventional Shapley Values"

15 / 15 papers shown
Title
Resisting Out-of-Distribution Data Problem in Perturbation of XAI
Resisting Out-of-Distribution Data Problem in Perturbation of XAI
Luyu Qiu
Yi Yang
Caleb Chen Cao
Jing Liu
Yueyuan Zheng
H. Ngai
J. H. Hsiao
Lei Chen
48
18
0
27 Jul 2021
On Locality of Local Explanation Models
On Locality of Local Explanation Models
Sahra Ghalebikesabi
Lucile Ter-Minassian
Karla Diaz-Ordaz
Chris Holmes
FedML
FAtt
52
39
0
24 Jun 2021
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual
  Predictions of Complex Models
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models
Tom Heskes
E. Sijben
I. G. Bucur
Tom Claassen
FAtt
TDI
105
152
0
03 Nov 2020
Fairwashing Explanations with Off-Manifold Detergent
Fairwashing Explanations with Off-Manifold Detergent
Christopher J. Anders
Plamen Pasliev
Ann-Kathrin Dombrowski
K. Müller
Pan Kessel
FAtt
FaML
44
97
0
20 Jul 2020
Improving LIME Robustness with Smarter Locality Sampling
Improving LIME Robustness with Smarter Locality Sampling
Sean Saito
Eugene Chua
Nicholas Capel
Rocco Hu
FAtt
AAML
41
22
0
22 Jun 2020
Shapley explainability on the data manifold
Shapley explainability on the data manifold
Christopher Frye
Damien de Mijolla
T. Begley
Laurence Cowton
Megan Stanley
Ilya Feige
FAtt
TDI
37
99
0
01 Jun 2020
Decision-Making with Auto-Encoding Variational Bayes
Decision-Making with Auto-Encoding Variational Bayes
Romain Lopez
Pierre Boyeau
Nir Yosef
Michael I. Jordan
Jeffrey Regier
BDL
393
10,591
0
17 Feb 2020
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation
  Methods
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods
Dylan Slack
Sophie Hilgard
Emily Jia
Sameer Singh
Himabindu Lakkaraju
FAtt
AAML
MLAU
70
819
0
06 Nov 2019
Feature relevance quantification in explainable AI: A causal problem
Feature relevance quantification in explainable AI: A causal problem
Dominik Janzing
Lenon Minorics
Patrick Blobaum
FAtt
CML
68
280
0
29 Oct 2019
The many Shapley values for model explanation
The many Shapley values for model explanation
Mukund Sundararajan
A. Najmi
TDI
FAtt
58
632
0
22 Aug 2019
An Introduction to Variational Autoencoders
An Introduction to Variational Autoencoders
Diederik P. Kingma
Max Welling
BDL
SSL
DRL
74
2,354
0
06 Jun 2019
Fooling Neural Network Interpretations via Adversarial Model
  Manipulation
Fooling Neural Network Interpretations via Adversarial Model Manipulation
Juyeon Heo
Sunghwan Joo
Taesup Moon
AAML
FAtt
93
203
0
06 Feb 2019
A Unified Approach to Interpreting Model Predictions
A Unified Approach to Interpreting Model Predictions
Scott M. Lundberg
Su-In Lee
FAtt
1.1K
21,906
0
22 May 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAtt
FaML
1.2K
16,976
0
16 Feb 2016
The Do-Calculus Revisited
The Do-Calculus Revisited
Judea Pearl
CML
137
166
0
16 Oct 2012
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