Paper Info
Title
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions
Abstract
We develop parallel predictive entropy search (PPES), a novel algorithm for Bayesian optimization of expensive black-box objective functions. At each iteration, PPES aims to select a batch of points which will maximize the information gain about the global maximizer of the objective. Well known strategies exist for suggesting a single evaluation point based on previous observations, while far fewer are known for selecting batches of points to evaluate in parallel. The few batch selection schemes that have been studied all resort to greedy methods to compute an optimal batch. To the best of our knowledge, PPES is the first non-greedy batch Bayesian optimization strategy. We demonstrate the benefit of this approach in optimization performance on both synthetic and real world applications, including problems in machine learning, rocket science and robotics.
Year
Venue
Field
2015
Annual Conference on Neural Information Processing Systems
Mathematical optimization,Global optimization,Computer science,Bayesian optimization,Information gain,Artificial intelligence,Rocket,Machine learning,Robotics
DocType
Volume
ISSN
Journal
abs/1511.07130
1049-5258
Citations 
PageRank 
References 
18
0.77
16
Authors
2
Name
Order
Citations
PageRank
Amar Shah11988.61
Zoubin Ghahramani2104551264.39