Bayesian Optimization for Categorical and Category-Specific Continuous Inputs

Understand

Many real-world functions are defined over both categorical and category-specific continuous variables and thus cannot be optimized by traditional Bayesian optimization (BO) methods.

• To optimize such functions, we propose a new method that formulates the problem as a multi-armed bandit problem, wherein each category corresponds to an arm with its reward distribution centered around the optimum of the objective function in continuous variables.
• Our goal is to identify the best arm and the maximizer of the corresponding continuous function simultaneously.
• Our algorithm uses a Thompson sampling scheme that helps connecting both multi-arm bandit and BO in a unified framework.