Information-based approaches for Bayesian optimization.

Опубликовано 22 июня 2016, 2:54

Bayesian optimization provides a principled, probabilistic approach for global optimization. In this talk I will give a brief overview of Bayesian optimization and then provide details on novel, information-theoretic approaches to this problem. In particular I will detail an algorithm we have developed called Predictive Entropy Search (PES) which maximizes the expected information gained with respect to the global maximum at every iteration. This reformulation allows PES to obtain approximations that are both more accurate and efficient than other alternatives. Finally, this approach also allows one to easily incorporate additional constraints that are much more problematic for alternative methods.