PS-DME is a new framework that controls post-selection false coverage rate for distributional KPI estimates via e-values and is provably more sample-efficient than data splitting under explicit conditions.
Comparing three learn-then-test paradigms in a multivariate normal means problem.arXiv preprint arXiv:2601.07764
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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.
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Post-Selection Distributional Model Evaluation
PS-DME is a new framework that controls post-selection false coverage rate for distributional KPI estimates via e-values and is provably more sample-efficient than data splitting under explicit conditions.
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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.