BOHM extracts multi-resolution attribution trees from existing routing weights in hierarchical AI systems, providing zero-cost explanations that correlate with SHAP when routing is near-optimal.
A unified approach to interpreting model predictions
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4representative citing papers
AIM is a new saliency-guided adversarial feature replacement method to evaluate faithfulness of saliency maps and reliability of masking operators on image, audio, and EEG tasks.
Quadrature-TreeSHAP computes Shapley values and higher-order interactions for tree ensembles via 8-point Gauss-Legendre quadrature on a Banzhaf polynomial, removing depth dependence while reaching machine precision and delivering speedups up to 1200x for interactions.
DeepPySR adds dynamic pruning, exponential Pareto selection, and hierarchical layers to PySR and reports better R²/F1 plus domain-aligned formulas on Feynman and seven real-world datasets.
citing papers explorer
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BOHM: Zero-Cost Hierarchical Attribution for Compound AI Systems
BOHM extracts multi-resolution attribution trees from existing routing weights in hierarchical AI systems, providing zero-cost explanations that correlate with SHAP when routing is near-optimal.
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AIM: Adversarial Information Masking for Faithfulness Evaluation of Saliency Maps
AIM is a new saliency-guided adversarial feature replacement method to evaluate faithfulness of saliency maps and reliability of masking operators on image, audio, and EEG tasks.
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Quadrature-TreeSHAP: Depth-Independent TreeSHAP and Shapley Interactions
Quadrature-TreeSHAP computes Shapley values and higher-order interactions for tree ensembles via 8-point Gauss-Legendre quadrature on a Banzhaf polynomial, removing depth dependence while reaching machine precision and delivering speedups up to 1200x for interactions.
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DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery
DeepPySR adds dynamic pruning, exponential Pareto selection, and hierarchical layers to PySR and reports better R²/F1 plus domain-aligned formulas on Feynman and seven real-world datasets.