Practical Bayesian Optimization of Objectives with Conditioning Variables

• URL: http://arxiv.org/abs/2002.09996v2
• Date: Mon, 2 Nov 2020 21:21:40 GMT
• Title: Practical Bayesian Optimization of Objectives with Conditioning Variables
• Authors: Michael Pearce, Janis Klaise, Matthew Groves
• Abstract summary: 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. Similarity across objectives boosts optimization of each objective in two ways. We propose a framework for conditional optimization: ConBO.
• Score: 1.0497128347190048
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 CIFAR-10, 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.

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