Predictive Entropy Search for Efficient Global Optimization of Black-box Functions

Understand

We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES).

• At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum.
• PES codifies this intractable acquisition function in terms of the expected reduction in the differential entropy of the predictive distribution.
• This reformulation allows PES to obtain approximations that are both more accurate and efficient than other alternatives such as Entropy Search (ES).