Kolmogorov n-width theory plus PRESS statistics yield closed-form optimal spline resolution; KORE estimates bias/noise scales from two pilots and matches CV performance with far fewer fits.
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A scalable generalized Bayesian logistic GP density regression is developed by replacing normalizing constants with the Hyvarinen score and using sparse variational inference.
Establishes sufficient conditions for causal direction identification in additive models with unobserved paths and introduces a sound, complete search algorithm.
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Solve for the Hyperparameter, Skip the Search: Kolmogorov-Optimal Scaling Laws for Spline Regression
Kolmogorov n-width theory plus PRESS statistics yield closed-form optimal spline resolution; KORE estimates bias/noise scales from two pilots and matches CV performance with far fewer fits.
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Logistic Gaussian process density regression: a generalized Bayesian approach
A scalable generalized Bayesian logistic GP density regression is developed by replacing normalizing constants with the Hyvarinen score and using sparse variational inference.
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Causal Additive Models with Unobserved Causal Paths and Backdoor Paths
Establishes sufficient conditions for causal direction identification in additive models with unobserved paths and introduces a sound, complete search algorithm.