Preprintgreen2017

Batched Large-scale Bayesian Optimization in High-dimensional Spaces

Zi Wang · Clement Gehring · Pushmeet Kohli · Stefanie Jegelka

114 citations0/0 files storedarXiv (Cornell University)

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Advanced Bandit Algorithms Research Advanced Multi-Objective Optimization Algorithms Gaussian Processes and Bayesian Inference

Abstract

Bayesian optimization (BO) has become an effective approach for black-box function optimization problems when function evaluations are expensive and the optimum can be achieved within a relatively small number of queries. However, many cases, such as the ones with high-dimensional inputs, may require a much larger number of observations for optimization. Despite an abundance of observations thanks to parallel experiments, current BO techniques have been limited to merely a few thousand observations. In this paper, we propose ensemble Bayesian optimization (EBO) to address three current challenges in BO simultaneously: (1) large-scale observations; (2) high dimensional input spaces; and (3) selections of batch queries that balance quality and diversity. The key idea of EBO is to operate on an ensemble of additive Gaussian process models, each of which possesses a randomized strategy to divide and conquer. We show unprecedented, previously impossible results of scaling up BO to tens of thousands of observations within minutes of computation.

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References

1964A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise 2012Entropy search for information-efficient global optimization 2016Bayesian Optimization in a Billion Dimensions via Random Embeddings 2014Predictive Entropy Search for Efficient Global Optimization of Black-box Functions 2012Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization 2015Batch Bayesian Optimization via Local Penalization 2015High Dimensional Bayesian Optimisation and Bandits via Additive Models 2013Parallel Gaussian process optimization with upper confidence bound and pure exploration

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2019Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly 2019A Framework for Bayesian Optimization in Embedded Subspaces 2021Bayesian Optimization in High-Dimensional Spaces: A Brief Survey 2023Surrogate Modeling for Bayesian Optimization Beyond a Single Gaussian Process 2020Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search

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