Preprintgreen2018

Bayesian Optimization of Combinatorial Structures

82 citations0/0 files storedarXiv (Cornell University)

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Advanced Bandit Algorithms Research Simulation Techniques and Applications Advanced Multi-Objective Optimization Algorithms

Abstract

The optimization of expensive-to-evaluate black-box functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences. The combinatorial explosion of the search space and costly evaluations pose challenges for current techniques in discrete optimization and machine learning, and critically require new algorithmic ideas. This article proposes, to the best of our knowledge, the first algorithm to overcome these challenges, based on an adaptive, scalable model that identifies useful combinatorial structure even when data is scarce. Our acquisition function pioneers the use of semidefinite programming to achieve efficiency and scalability. Experimental evaluations demonstrate that this algorithm consistently outperforms other methods from combinatorial and Bayesian optimization.

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References

2012Practical Bayesian Optimization of Machine Learning Algorithms 2015Taking the Human Out of the Loop: A Review of Bayesian Optimization 2010A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning 2016Bayesian Optimization in a Billion Dimensions via Random Embeddings 2014Predictive Entropy Search for Efficient Global Optimization of Black-box Functions 2010The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery

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2021High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces 2019High Dimensional Bayesian Optimization via Supervised Dimension Reduction 2020Combinatorial Surrogate-Assisted Optimization for Bus Stops Spacing Problem

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