title: Practical Bayesian Optimization of Objectives with Conditioning Variables
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 given a range of cities with different patient distributions,
  we optimize the ambulance locations conditioned on patient distribution. Given partitions of Cifar10,
  we optimize CNN hyperparameters for each partition. 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 a single
  point on one objective can provide benefit to all objectives. For this we propose a framework
  for conditional optimization: ConBO. This can be built on top of a range of acquisition functions
  and we propose a new Hybrid Knowledge Gradient acquisition function. The resulting method is intuitive and
  theoretically grounded, performs either similar to or significantly better than recently published works
  on a range of problems, and is easily parallelized to collect a batch of points.
authors: Michael Arthur Leopold Pearce Janis Klaise Matthew Groves
hIndex: Janis Klaise 5