Cover Image

shapiq: Shapley Interactions for Machine Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Muschalik, Maximilian, Baniecki, Hubert, Fumagalli, Fabian, Kolpaczki, Patrick, Hammer, Barbara, Hüllermeier, Eyke
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Explaining Similarity in Vision-Language Encoders with Weighted Banzhaf Interactions
by: Baniecki, Hubert, et al.
Published: (2025)

Proxy-Based Approximation of Shapley and Banzhaf Interactions
by: Thies, Santo M. A. R., et al.
Published: (2026)

Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles
by: Muschalik, Maximilian, et al.
Published: (2024)

Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks
by: Muschalik, Maximilian, et al.
Published: (2025)

SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification
by: Kolpaczki, Patrick, et al.
Published: (2024)

Approximating the Shapley Value without Marginal Contributions
by: Kolpaczki, Patrick, et al.
Published: (2023)

Antithetic Sampling for Top-k Shapley Identification
by: Kolpaczki, Patrick, et al.
Published: (2025)

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

Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory
by: Fumagalli, Fabian, et al.
Published: (2024)

Shapley Value Approximation Based on k-Additive Games
by: Pelegrina, Guilherme Dean, et al.
Published: (2025)

Attributions All the Way Down? The Metagame of Interpretability
by: Baniecki, Hubert, et al.
Published: (2026)

Exactly Computing do-Shapley Values
by: Witter, R. Teal, et al.
Published: (2026)

Pairwise Difference Learning for Classification
by: Belaid, Mohamed Karim, et al.
Published: (2024)

No learning rates needed: Introducing SALSA -- Stable Armijo Line Search Adaptation
by: Kenneweg, Philip, et al.
Published: (2024)

Functional Decomposition and Shapley Interactions for Interpreting Survival Models
by: Langbein, Sophie Hanna, et al.
Published: (2026)

An Odd Estimator for Shapley Values
by: Fumagalli, Fabian, et al.
Published: (2026)

Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?
by: Jürgens, Mira, et al.
Published: (2024)

Efficient Credal Prediction through Decalibration
by: Hofman, Paul, et al.
Published: (2026)

Uncertainty Quantification as a Principled Foundation for Explainable Artificial Intelligence: A Case Study of Counterfactual Explanations
by: Sokol, Kacper, et al.
Published: (2025)

Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection
by: Spliethöver, Maximilian, et al.
Published: (2025)

A calibration test for evaluating set-based epistemic uncertainty representations
by: Jürgens, Mira, et al.
Published: (2025)

Adversarial attacks and defenses in explainable artificial intelligence: A survey
by: Baniecki, Hubert, et al.
Published: (2023)

ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data
by: Margraf, Valentin, et al.
Published: (2024)

Calibrated Preference Learning: The Case of Label Ranking
by: Thies, Santo M. A. R., et al.
Published: (2026)

Interpreting CLIP with Hierarchical Sparse Autoencoders
by: Zaigrajew, Vladimir, et al.
Published: (2025)

Extending Fair Null-Space Projections for Continuous Attributes to Kernel Methods
by: Störck, Felix, et al.
Published: (2025)

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

Adjusted Count Quantification Learning on Graphs
by: Damke, Clemens, et al.
Published: (2025)

Uncertainty Quantification for Machine Learning: One Size Does Not Fit All
by: Hofman, Paul, et al.
Published: (2025)

SwordBench: Evaluating Orthogonality of Steering Image Representations
by: Zaigrajew, Vladimir, et al.
Published: (2026)

Large-Batch, Iteration-Efficient Neural Bayesian Design Optimization
by: Ansari, Navid, et al.
Published: (2023)

Continuous Fair SMOTE -- Fairness-Aware Stream Learning from Imbalanced Data
by: Lammers, Kathrin, et al.
Published: (2025)

Beyond Shapley Values: Cooperative Games for the Interpretation of Machine Learning Models
by: Idrissi, Marouane Il, et al.
Published: (2025)

Towards Understanding the Influence of Training Samples on Explanations
by: Artelt, André, et al.
Published: (2024)

Fairness-Enhancing Ensemble Classification in Water Distribution Networks
by: Strotherm, Janine, et al.
Published: (2024)

survex: an R package for explaining machine learning survival models
by: Spytek, Mikołaj, et al.
Published: (2023)

CLANE: Continual Learning of Actions on Neuromorphic Hardware from Event Cameras
by: Hajizada, Elvin, et al.
Published: (2026)

Conceptualizing Uncertainty: A Concept-based Approach to Explaining Uncertainty
by: Roberts, Isaac, et al.
Published: (2025)

Co-Exploration and Co-Exploitation via Shared Structure in Multi-Task Bandits
by: Mukherjee, Sumantrak, et al.
Published: (2025)