Author

M. Balandat

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#artificial intelligence Preprint Sep 2026

Efficiently Estimating Optimal Hyperparameter Scaling Laws through Power-Law Entropy Search

This work introduces Power-Law Entropy Search (PLES), a computational cost-aware acquisition function built on multi-fidelity Bayesian optimization that efficiently estimates optimal hyperparameter scaling laws through adaptive experimentation using less than one-tenth of the computational budget required by convention...

Zhiliang Chen, S. Ament, David Eriksson et al. · 1 citation

Preprint Jul 2026

Pitfalls and Remedies for Multi-Task Bayesian Optimization

Bayesian optimization routinely warm-starts a target experiment with data from related source tasks, and the multi-task Gaussian process is the textbook surrogate for the job, but it is found that it misestimates the cross-task correlation even in the simplest non-trivial case.

Carl Hvarfner, Sam Daulton, M. Balandat et al. · 0 citations