Scalable Bayesian Optimization with Memory Retention

DOI

Bibliographic Information

Other Title
  • 過去記憶を用いたスケーラブルなベイズ最適化

Description

<p>Bayesian optimization is a method for the global optimization of black-box functions as few evaluation as possible. It utilizes Gaussian processes to efficiently select parameters to be evaluated. However, it is not scalable because Gaussian processes scale cubically with the number of iterations. In this work, we propose a method for scalable Bayesian optimization by leveraging models used in past iterations, which we call past memory. This technique enables us to t Gaussian processes to only input-output pairs near the previously selected input parameter. In experiments, we show our proposed method outperforms naive Bayesian optimization in terms of optimization performance with limited time budget.</p>

Journal

Keywords

Details 詳細情報について

  • CRID
    1390564238097601792
  • NII Article ID
    130007658334
  • DOI
    10.11517/pjsai.jsai2019.0_1j3j202
  • ISSN
    27587347
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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