Bayesian optimization

Sat, 18 Jul 2026 09:47:51 +0000

Definition

Bayesian optimization uses a probabilistic surrogate model, typically a Gaussian Process, to model the objective function. It employs an acquisition function to balance exploration and exploitation, selecting the next evaluation point that maximizes expected improvement. This method is highly efficient for tuning hyperparameters in machine learning models where each training run is computationally costly, requiring fewer evaluations than grid or random search to find near-optimal configurations.

Summary

A sequential design strategy for global optimization of black-box functions that are expensive to evaluate.

Surrogate Models on English AI Terms Dictionary

Bayesian optimization

Definition

Summary