Pre-training of Bayesian Optimization Algorithm through Bayesian Optimization

Key points

• However, BO algorithms often involve parameters that must be specified in advance, and their performance can strongly depend on these choices.1
• We propose a framework for optimizing such parameters using sample paths drawn from a Gaussian process (GP) inferred from the information available at the start of BO.1
• By running the BO algorithm on the generated sample paths, we obtain an empirical estimate of its expected cumulative regret for a given parameter configuration.1
• Through experiments, we demonstrate that the proposed framework can effectively select parameter configurations that achieve strong performance among a range of candidate configurations.1

Sources (1)

• [1]Pre-training of Bayesian Optimization Algorithm through Bayesian Optimization
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 02:53 PM

  Bayesian optimization (BO) is widely used as a standard approach for expensive black-box optimization.

  However, BO algorithms often involve parameters that must be specified in advance, and their performance can strongly depend on these choices.

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