Multi-task Gaussian process upper confidence bound for hyperparameter tuning and its application for simulation studies of additive manufacturing

Bo Shen, Raghav Gnanasambandam, Rongxuan Wang, Zhenyu James Kong

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In many scientific and engineering applications, Bayesian Optimization (BO) is a powerful tool for hyperparameter tuning of a machine learning model, materials design and discovery, etc. Multi-task BO is a general method to efficiently optimize multiple different, but correlated, “black-box” functions. The objective of this work is to develop an algorithm for multi-task BO with automatic task selection so that only one task evaluation is needed per query round. Specifically, a new algorithm, namely, Multi-Task Gaussian Process Upper Confidence Bound (MT-GPUCB), is proposed to achieve this objective. The MT-GPUCB is a two-step algorithm, where the first step chooses which query point to evaluate, and the second step automatically selects the most informative task to evaluate. Under the bandit setting, a theoretical analysis is provided to show that our proposed MT-GPUCB is no-regret under some mild conditions. Our proposed algorithm is verified experimentally on a range of synthetic functions. In addition, our algorithm is applied to Additive Manufacturing simulation software, namely, Flow-3D Weld, to determine material property values, ensuring the quality of simulation output. The results clearly show the advantages of our query strategy for both design point and task.

Original languageEnglish (US)
Pages (from-to)496-508
Number of pages13
JournalIISE Transactions
Volume55
Issue number5
DOIs
StatePublished - 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Keywords

  • Multi-task Gaussian process upper confidence bound
  • automatic task selection
  • hyperparameter tuning
  • no-regret

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