AGOP-IxG filters per-sample gradients with a top-K truncated average gradient outer product matrix and outperforms SHAP, Integrated Gradients, InputXGradient, and LIME on Spearman correlation and noise mass across three synthetic tabular tasks while running 350-1650x faster.
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AGOP-IxG: A Gradient Covariance Filter for Local Feature Attribution on Tabular Data, with a Controlled Benchmark
AGOP-IxG filters per-sample gradients with a top-K truncated average gradient outer product matrix and outperforms SHAP, Integrated Gradients, InputXGradient, and LIME on Spearman correlation and noise mass across three synthetic tabular tasks while running 350-1650x faster.