arXiv Machine Learning By Anh Tuan Nguyen, Viet Anh Nguyen

Tight Bounds for Data-driven Multiple Hyper-parameter Tuning with Structured Loss Function

Read the original on arXiv Machine Learning →

arXiv:2608. 17343v1 Announce Type: new Abstract: Data-driven algorithm design frames hyperparameter tuning as a statistical learning problem, but establishing generalization guarantees remains challenging due to the implicit, non-smooth dependence of model performance on hyperparameters.

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arXiv Machine Learning
Jun 10

The hyper-scaled NLP bound for maximum-entropy remote sampling

arXiv:2601. 20970v3 Announce Type: replace-cross Abstract: The maximum-entropy remote sampling problem (MERSP) is to select a subset of $s$ random variables from a set of $n$ random variables, so as to maximize the information concerning a set of target random variables that are not directly observable.

By Gabriel Ponte, Marcia Fampa, Jon Lee