ShapShift explains prediction shifts by attributing them to changes in conditional probabilities of tree-defined subgroups via conditional Shapley values, with exact computation for single trees and surrogate extensions for other models.
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Directional Chebyshev harmonics enable spectral path regression for tabular data with closed-form training, competitive accuracy, and explicit interpretability.
Max-plus neural networks enable tracing each output to one dominant neuron, allowing a pixel fragility measure that provides more useful explanations than SHAP or Integrated Gradients on medical images.
citing papers explorer
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ShapShift: Explaining Model Prediction Shifts with Subgroup Conditional Shapley Values
ShapShift explains prediction shifts by attributing them to changes in conditional probabilities of tree-defined subgroups via conditional Shapley values, with exact computation for single trees and surrogate extensions for other models.
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Spectral Path Regression: Directional Chebyshev Harmonics for Interpretable Tabular Learning
Directional Chebyshev harmonics enable spectral path regression for tabular data with closed-form training, competitive accuracy, and explicit interpretability.
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On the explainability of max-plus neural networks
Max-plus neural networks enable tracing each output to one dominant neuron, allowing a pixel fragility measure that provides more useful explanations than SHAP or Integrated Gradients on medical images.