Paper gives an exact finite-sample identity for CV variance
K-fold is first-order variance optimal, leave-one-out is smallest among K-fold schemes, and the estimate is asymptotically normal.
Stories per day · 30 days
Relatedauc1cross-validation1empirical-bayes1gaussian-mixtures1minimax1model-evaluation1npmle1variance1
6 stories, newest first · one card per story
Stories tagged #statistical-learning
NPMLE shown minimax optimal for univariate Gaussian mixtures
With compact support, a constrained NPMLE reaches the slightly faster rate log(n)/(n log log n).
Learning by Compression: Risk Estimates Attain the Law-of-Large-Numbers Rate
Compression-based learning risk bounds match the law-of-large-numbers rate without a logarithmic penalty.
Holdout Best-of-N: Unbiased Evaluation and Its Cost
Holdout Best-of-N unbiasedly estimates selection reward only when selector depth is below the score count.
Fast Learning Rates for Physics-Informed Kernel Methods
Theoretical analysis proves finite-sample learning rates for physics-informed kernel estimators, showing differential observations can improve rates from n^-1/4 to n^-1/2.
Optimal Rates for Agentic Networked Information Aggregation
Researchers close the Kearns–Roth–Ryu gap for agentic networked information aggregation, proving excess error is constant up to depth M^2 then Θ(M^2/D).