A Tweedie calculus framework is introduced that proves the existence of a unique continuous linear functional governing direct expressions for conditional expectations of latent variables from observed densities in additive noise models.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
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.
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Tweedie Calculus
A Tweedie calculus framework is introduced that proves the existence of a unique continuous linear functional governing direct expressions for conditional expectations of latent variables from observed densities in additive noise models.
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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.