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Faith-Shap: The Faithful Shapley Interaction Index
v1v2v3 (latest)

Faith-Shap: The Faithful Shapley Interaction Index

2 March 2022
Che-Ping Tsai
Chih-Kuan Yeh
Pradeep Ravikumar
    TDI
ArXiv (abs)PDFHTML

Papers citing "Faith-Shap: The Faithful Shapley Interaction Index"

34 / 34 papers shown
Title
ProxySPEX: Inference-Efficient Interpretability via Sparse Feature Interactions in LLMs
ProxySPEX: Inference-Efficient Interpretability via Sparse Feature Interactions in LLMs
Landon Butler
Abhineet Agarwal
Justin Singh Kang
Yigit Efe Erginbas
Bin Yu
Kannan Ramchandran
141
0
0
23 May 2025
SPEX: Scaling Feature Interaction Explanations for LLMs
SPEX: Scaling Feature Interaction Explanations for LLMs
Justin Singh Kang
Landon Butler
Abhineet Agarwal
Yigit Efe Erginbas
Ramtin Pedarsani
Kannan Ramchandran
Bin Yu
VLMLRM
162
2
0
20 Feb 2025
Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection
Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection
Maximilian Spliethover
Tim Knebler
Fabian Fumagalli
Maximilian Muschalik
Barbara Hammer
Eyke Hüllermeier
Henning Wachsmuth
188
1
0
10 Feb 2025
Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory
Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory
Fabian Fumagalli
Maximilian Muschalik
Eyke Hüllermeier
Barbara Hammer
J. Herbinger
FAtt
199
4
0
22 Dec 2024
Explainable and Interpretable Multimodal Large Language Models: A
  Comprehensive Survey
Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey
Yunkai Dang
Kaichen Huang
Jiahao Huo
Yibo Yan
Shijie Huang
...
Kun Wang
Yong Liu
Jing Shao
Hui Xiong
Xuming Hu
LRM
170
22
0
03 Dec 2024
A Bayesian explanation of machine learning models based on modes and
  functional ANOVA
A Bayesian explanation of machine learning models based on modes and functional ANOVA
Quan Long
29
1
0
05 Nov 2024
Why pre-training is beneficial for downstream classification tasks?
Why pre-training is beneficial for downstream classification tasks?
Xin Jiang
Xu Cheng
Zechao Li
70
0
0
11 Oct 2024
SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference
SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference
Yuan Zhang
Chun-Kai Fan
Junpeng Ma
Wenzhao Zheng
Tao Huang
...
Denis A. Gudovskiy
Tomoyuki Okuno
Yohei Nakata
Kurt Keutzer
Shanghang Zhang
VLM
68
59
0
06 Oct 2024
shapiq: Shapley Interactions for Machine Learning
shapiq: Shapley Interactions for Machine Learning
Maximilian Muschalik
Hubert Baniecki
Fabian Fumagalli
Patrick Kolpaczki
Barbara Hammer
Eyke Hüllermeier
TDI
66
13
0
02 Oct 2024
META-ANOVA: Screening interactions for interpretable machine learning
META-ANOVA: Screening interactions for interpretable machine learning
Daniel A. Serino
Marc L. Klasky
Chanmoo Park
Dongha Kim
Yongdai Kim
72
0
0
02 Aug 2024
nn2poly: An R Package for Converting Neural Networks into Interpretable
  Polynomials
nn2poly: An R Package for Converting Neural Networks into Interpretable Polynomials
Pablo Morala
Jenny Alexandra Cifuentes
R. Lillo
Iñaki Ucar
121
0
0
03 Jun 2024
Explaining Graph Neural Networks via Structure-aware Interaction Index
Explaining Graph Neural Networks via Structure-aware Interaction Index
Ngoc H. Bui
Hieu Trung Nguyen
Viet Anh Nguyen
Rex Ying
FAtt
76
4
0
23 May 2024
KernelSHAP-IQ: Weighted Least-Square Optimization for Shapley
  Interactions
KernelSHAP-IQ: Weighted Least-Square Optimization for Shapley Interactions
Fabian Fumagalli
Maximilian Muschalik
Patrick Kolpaczki
Eyke Hüllermeier
Barbara Hammer
103
7
0
17 May 2024
Using Shapley interactions to understand how models use structure
Using Shapley interactions to understand how models use structure
Divyansh Singhvi
Andrej Erkelens
Raghav Jain
Diganta Misra
Isabel Papadimitriou
Naomi Saphra
52
0
0
19 Mar 2024
Interpretable Machine Learning for Survival Analysis
Interpretable Machine Learning for Survival Analysis
Sophie Hanna Langbein
Mateusz Krzyzinski
Mikolaj Spytek
Hubert Baniecki
P. Biecek
Marvin N. Wright
85
2
0
15 Mar 2024
Succinct Interaction-Aware Explanations
Succinct Interaction-Aware Explanations
Sascha Xu
Joscha Cuppers
Jilles Vreeken
FAtt
75
0
0
08 Feb 2024
Learning to Understand: Identifying Interactions via the Möbius
  Transform
Learning to Understand: Identifying Interactions via the Möbius Transform
Justin Singh Kang
Yigit Efe Erginbas
Landon Butler
Ramtin Pedarsani
Kannan Ramchandran
92
4
0
04 Feb 2024
Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions
  for Tree Ensembles
Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles
Maximilian Muschalik
Fabian Fumagalli
Barbara Hammer
Eyke Hüllermeier
TDI
107
18
0
22 Jan 2024
Sample based Explanations via Generalized Representers
Sample based Explanations via Generalized Representers
Che-Ping Tsai
Chih-Kuan Yeh
Pradeep Ravikumar
FAtt
93
9
0
27 Oct 2023
Bayesian Regression Markets
Bayesian Regression Markets
Thomas Falconer
J. Kazempour
Pierre Pinson
145
3
0
23 Oct 2023
Explaining Interactions Between Text Spans
Explaining Interactions Between Text Spans
Sagnik Ray Choudhury
Pepa Atanasova
Isabelle Augenstein
56
2
0
20 Oct 2023
Conditional expectation network for SHAP
Conditional expectation network for SHAP
Ronald Richman
M. Wüthrich
FAttBDL
47
3
0
20 Jul 2023
Four Axiomatic Characterizations of the Integrated Gradients Attribution
  Method
Four Axiomatic Characterizations of the Integrated Gradients Attribution Method
Daniel Lundstrom
Meisam Razaviyayn
FAtt
38
3
0
23 Jun 2023
Distributing Synergy Functions: Unifying Game-Theoretic Interaction
  Methods for Machine-Learning Explainability
Distributing Synergy Functions: Unifying Game-Theoretic Interaction Methods for Machine-Learning Explainability
Daniel Lundstrom
Meisam Razaviyayn
FAtt
57
1
0
04 May 2023
Where We Have Arrived in Proving the Emergence of Sparse Symbolic
  Concepts in AI Models
Where We Have Arrived in Proving the Emergence of Sparse Symbolic Concepts in AI Models
Qihan Ren
Maximilian Brunner
Wen Shen
S. Mintchev
91
12
0
03 May 2023
Technical Note: Defining and Quantifying AND-OR Interactions for
  Faithful and Concise Explanation of DNNs
Technical Note: Defining and Quantifying AND-OR Interactions for Faithful and Concise Explanation of DNNs
Mingjie Li
Quanshi Zhang
69
11
0
26 Apr 2023
SHAP-IQ: Unified Approximation of any-order Shapley Interactions
SHAP-IQ: Unified Approximation of any-order Shapley Interactions
Fabian Fumagalli
Maximilian Muschalik
Patrick Kolpaczki
Eyke Hüllermeier
Barbara Hammer
127
30
0
02 Mar 2023
Does a Neural Network Really Encode Symbolic Concepts?
Does a Neural Network Really Encode Symbolic Concepts?
Mingjie Li
Quanshi Zhang
98
31
0
25 Feb 2023
Approximating the Shapley Value without Marginal Contributions
Approximating the Shapley Value without Marginal Contributions
Patrick Kolpaczki
Viktor Bengs
Maximilian Muschalik
Eyke Hüllermeier
FAttTDI
156
26
0
01 Feb 2023
Training Data Influence Analysis and Estimation: A Survey
Training Data Influence Analysis and Estimation: A Survey
Zayd Hammoudeh
Daniel Lowd
TDI
117
101
0
09 Dec 2022
From Shapley Values to Generalized Additive Models and back
From Shapley Values to Generalized Additive Models and back
Sebastian Bordt
U. V. Luxburg
FAttTDI
168
43
0
08 Sep 2022
Unifying local and global model explanations by functional decomposition
  of low dimensional structures
Unifying local and global model explanations by functional decomposition of low dimensional structures
M. Hiabu
Josephine T. Meyer
Marvin N. Wright
FAtt
82
21
0
12 Aug 2022
Defining and Quantifying the Emergence of Sparse Concepts in DNNs
Defining and Quantifying the Emergence of Sparse Concepts in DNNs
Jie Ren
Mingjie Li
Qirui Chen
Huiqi Deng
Quanshi Zhang
120
33
0
11 Nov 2021
"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
FAttFaML
1.3K
17,197
0
16 Feb 2016
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