A kernel-copula embedding statistic equals zero exactly when causal dependence between X and Y is stable and is strictly positive otherwise, with a near-linear estimator and convergence rates provided.
Advances in neural information processing systems , volume=
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Temperature scaling of density-matrix eigenvalues from LLM semantic embeddings optimizes proper-score calibration and corrects systematic overconfidence so entropy equals risk.
A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.
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Detecting Changes in Causal Dependence with Kernels and Copulas
A kernel-copula embedding statistic equals zero exactly when causal dependence between X and Y is stable and is strictly positive otherwise, with a near-linear estimator and convergence rates provided.
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Eigenvalue Calibration for Semantic Embeddings of Large Language Models
Temperature scaling of density-matrix eigenvalues from LLM semantic embeddings optimizes proper-score calibration and corrects systematic overconfidence so entropy equals risk.
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A Semi-Supervised Kernel Two-Sample Test
A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.