Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian Processes

Understand

Bayesian Optimization (BO) methods are useful for optimizing functions that are expen- sive to evaluate, lack an analytical expression and whose evaluations can be contaminated by noise.

• These methods rely on a probabilistic model of the objective function, typically a Gaussian process (GP), upon which an acquisition function is built.
• The acquisition function guides the optimization process and measures the expected utility of performing an evaluation of the objective at a new point.
• GPs assume continous input variables.