Venue: arXiv
Year: 2020
Paper: https://arxiv.org/abs/2002.09996
Abstract
Bayesian optimization is a class of data efficient model based algorithms typically focused on global optimization. We consider the more general case where a user is faced with multiple problems that each need to be optimized conditional on a state variable, for example we optimize the location of ambulances conditioned on patient distribution given a range of cities with different patient distributions. Similarity across objectives boosts optimization of each objective in two ways: in modelling by data sharing across objectives, and also in acquisition by quantifying how all objectives benefit from a single point on one objective. For this we propose ConBO, a novel efficient algorithm that is based on a new hybrid Knowledge Gradient method, that outperforms recently published works on synthetic and real world problems, and is easily parallelized to collecting a batch of points.