Additive Tree-Structured Conditional Parameter Spaces in Bayesian Optimization: A Novel Covariance Function and a...

Area of Science:

• Machine Learning
• Optimization Algorithms
• Computational Science

Background:

• Bayesian optimization (BO) is a powerful technique for optimizing expensive-to-evaluate black-box functions.
• Current model-based optimization in conditional parameter spaces often relies on tree-based structures.
• Generalizing additive assumptions to tree-structured functions presents an opportunity for improved performance.

Purpose of the Study:

• To generalize the additive assumption to tree-structured functions for enhanced Bayesian optimization.
• To develop a novel additive tree-structured covariance function for conditional parameter spaces.
• To create a parallel algorithm for optimizing acquisition functions in low-dimensional spaces.

Main Methods:

• Proposed an additive tree-structured covariance function.
• Incorporated parameter space structure and additive assumption into the BO loop.
• Developed a parallel acquisition function optimization algorithm suitable for low-dimensional spaces.

Main Results:

• The proposed additive tree-structured covariance function demonstrated improved sample-efficiency and flexibility.
• The parallel optimization algorithm effectively reduced the dimensionality of acquisition function optimization.
• Experimental results showed significant outperformance compared to state-of-the-art methods like SMAC and TPE.

Conclusions:

• The novel additive tree-structured covariance function advances Bayesian optimization for conditional parameter spaces.
• The developed parallel optimization approach offers a more efficient way to handle complex optimization problems.
• This method shows strong potential for applications in machine learning, particularly in neural network optimization.