GKCM generalizes kernel CI testing to arbitrary regression models, provides uniform asymptotic level guarantees under stated conditions, and outperforms state-of-the-art methods in simulations when using tree-based regressors.
Publisher: De Gruyter
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Sublinearly structured DNNs attain feature-learning consistency and universal approximation for hierarchically compositional functions, with popular CNNs fitting this structure on image benchmarks.
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The Generalised Kernel Covariance Measure
GKCM generalizes kernel CI testing to arbitrary regression models, provides uniform asymptotic level guarantees under stated conditions, and outperforms state-of-the-art methods in simulations when using tree-based regressors.