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Statistically Valid Post-Training Hyperparameter Selection: From Tuning to Guarantees
Post-training hyperparameter selection is a critical step in the deployment of modern artificial intelligence systems, given the need to tune degrees of freedom of pre-trained models such as inference-time parameters, implementation-level settings, and thresholds driving decision rules. Despite its
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