Presents a unified statistical framework using the learn-then-test paradigm for hyperparameter selection that provides explicit finite-sample guarantees on application-specific reliability requirements via hypothesis testing.
Randomization inference: Theory and applications
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Statistically Valid Hyperparameter Selection: From Tuning to Guarantees
Presents a unified statistical framework using the learn-then-test paradigm for hyperparameter selection that provides explicit finite-sample guarantees on application-specific reliability requirements via hypothesis testing.