LandscapeSHAP: Which Persistent Homology Class Gets the Credit?

arXiv:2609.31469 · math.AT, cs.LG · Submitted 2026-09-25 · Read on arXiv

math.AT, cs.LG

Submitted: 2026-09-25

Updated: 2026-09-25

Comments: 38 pages, 10 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Shapley values — 113× in this paper · explained in 2 episodes
• Persistent Homology — 25× in this paper · explained in 3 episodes
• Point Clouds — 20× in this paper · explained in 2 episodes
• Axioms — 8× in this paper · explained in 2 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• alpha — 7× in this paper · explained in 1 episode
• Explainability — 6× in this paper · explained in 3 episodes
• Hilbert Space — 4× in this paper · explained in 2 episodes
• Additivity — 2× in this paper · explained in 1 episode
• Embeddings — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Inverse Problem — 2× in this paper · explained in 2 episodes
• Model-Agnostic — 2× in this paper · explained in 2 episodes
• Support Vector Machine — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Credit assignment — 1× in this paper · explained in 3 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Topological Data Analysis (TDA) — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Wasserstein Distance — 1× in this paper · explained in 4 episodes

Sources

• Explainable topological data analysis using persistence heatmaps
• Bounding the Estimation Error of Sampling-based Shapley Value Approximation
• Persformer: A Transformer Architecture for Topological Machine Learning
• Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

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