:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Letoffe, Olivier, Huang, Xuanxiang, Marques-Silva, Joao
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2412.13866
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Towards trustable SHAP scores
by: Letoffe, Olivier, et al.
Published: (2024)

From SHAP Scores to Feature Importance Scores
by: Letoffe, Olivier, et al.
Published: (2024)

Rigorous Feature Importance Scores based on Shapley Value and Banzhaf Index
by: Huang, Xuanxiang, et al.
Published: (2025)

The Explanation Game -- Rekindled (Extended Version)
by: Marques-Silva, Joao, et al.
Published: (2025)

Towards Rigorous Explainability by Feature Attribution
by: Létoffé, Olivier, et al.
Published: (2026)

Explainability is NOT a Game
by: Marques-Silva, Joao, et al.
Published: (2023)

Rigorous Explanations for Tree Ensembles
by: Izza, Yacine, et al.
Published: (2026)

A Refutation of Shapley Values for Explainability
by: Huang, Xuanxiang, et al.
Published: (2023)

From Robustness to Explainability and Back Again
by: Huang, Xuanxiang, et al.
Published: (2023)

Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation
by: Izza, Yacine, et al.
Published: (2024)

SHAP-based Explanations are Sensitive to Feature Representation
by: Hwang, Hyunseung, et al.
Published: (2025)

PolySHAP: Extending KernelSHAP with Interaction-Informed Polynomial Regression
by: Fumagalli, Fabian, et al.
Published: (2026)

The Distributional Uncertainty of the SHAP score in Explainable Machine Learning
by: Cifuentes, Santiago, et al.
Published: (2024)

Interaction Tensor SHAP
by: Hasegawa, Hiroki, et al.
Published: (2025)

CS-SHAP: Extending SHAP to Cyclic-Spectral Domain for Better Interpretability of Intelligent Fault Diagnosis
by: Chen, Qian, et al.
Published: (2025)

CNN-TFT explained by SHAP with multi-head attention weights for time series forecasting
by: Stefenon, Stefano F., et al.
Published: (2025)

Explanation Multiplicity in SHAP: Characterization and Assessment
by: Hwang, Hyunseung, et al.
Published: (2026)

Locally-Minimal Probabilistic Explanations
by: Izza, Yacine, et al.
Published: (2023)

Can Global XAI Methods Reveal Injected Bias in LLMs? SHAP vs Rule Extraction vs RuleSHAP
by: Sovrano, Francesco
Published: (2025)

On the Tractability of SHAP Explanations under Markovian Distributions
by: Marzouk, Reda, et al.
Published: (2024)

ContextualSHAP : Enhancing SHAP Explanations Through Contextual Language Generation
by: Dwiyanti, Latifa, et al.
Published: (2025)

Efficient & Correct Predictive Equivalence for Decision Trees
by: Marques-Silva, Joao, et al.
Published: (2025)

Towards Piece-by-Piece Explanations for Chess Positions with SHAP
by: Spinnato, Francesco
Published: (2025)

The Cost of Relaxation: Evaluating the Error in Convex Neural Network Verification
by: Papamichail, Merkouris, et al.
Published: (2026)

Delivering Inflated Explanations
by: Izza, Yacine, et al.
Published: (2023)

HyperSHAP: Shapley Values and Interactions for Explaining Hyperparameter Optimization
by: Wever, Marcel, et al.
Published: (2025)

How to safely discard features based on aggregate SHAP values
by: Bhattacharjee, Robi, et al.
Published: (2025)

A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME
by: Salih, Ahmed, et al.
Published: (2023)

A Polynomial-Time Axiomatic Alternative to SHAP for Feature Attribution
by: Hiraki, Kazuhiro, et al.
Published: (2026)

Fooling SHAP with Output Shuffling Attacks
by: Yuan, Jun, et al.
Published: (2024)

Breast Cancer Classification Using Gradient Boosting Algorithms Focusing on Reducing the False Negative and SHAP for Explainability
by: Pinheiro, João Manoel Herrera, et al.
Published: (2024)

KernelSHAP-IQ: Weighted Least-Square Optimization for Shapley Interactions
by: Fumagalli, Fabian, et al.
Published: (2024)

Enhancing SHAP Explainability for Diagnostic and Prognostic ML Models in Alzheimer Disease
by: Guillén, Pablo, et al.
Published: (2026)

CQD-SHAP: Explainable Complex Query Answering via Shapley Values
by: Abbasi, Parsa, et al.
Published: (2025)

Incentivized Lipschitz Bandits
by: Chakraborty, Sourav, et al.
Published: (2025)

Shaping Up SHAP: Enhancing Stability through Layer-Wise Neighbor Selection
by: Kelodjou, Gwladys, et al.
Published: (2023)

DeltaSHAP: Explaining Prediction Evolutions in Online Patient Monitoring with Shapley Values
by: Kim, Changhun, et al.
Published: (2025)

On Trustworthy Rule-Based Models and Explanations
by: Siala, Mohamed, et al.
Published: (2025)

Feature contamination: Neural networks learn uncorrelated features and fail to generalize
by: Zhang, Tianren, et al.
Published: (2024)

Choose Your Explanation: A Comparison of SHAP and GradCAM in Human Activity Recognition
by: Tempel, Felix, et al.
Published: (2024)