Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
This work addresses the challenge of global optimization for researchers and practitioners in fields like machine learning, finance, and robotics, offering an incremental improvement over existing methods.
The paper tackles the problem of efficiently optimizing black-box functions by proposing Predictive Entropy Search (PES), a novel information-theoretic approach for Bayesian optimization that selects points to maximize expected information gain about the global maximum, leading to significant performance improvements in synthetic and real-world applications.
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). Furthermore, PES can easily perform a fully Bayesian treatment of the model hyperparameters while ES cannot. We evaluate PES in both synthetic and real-world applications, including optimization problems in machine learning, finance, biotechnology, and robotics. We show that the increased accuracy of PES leads to significant gains in optimization performance.