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Bilevel Optimization of Regularization Hyperparameters in Machine Learning

In: Bilevel Optimization

Author

Listed:
  • Takayuki Okuno

    (RIKEN AIP)

  • Akiko Takeda

    (The University of Tokyo)

Abstract

Most of the main machine learning (ML) models are equipped with parameters that need to be prefixed. Such parameters are often called hyperparameters. Needless to say, prediction performance of ML models significantly relies on the choice of hyperparameters. Hence, establishing methodology for properly tuning hyperparameters has been recognized as one of the most crucial matters in ML. In this chapter, we introduce the role of bilevel optimization in the context of selecting hyperparameters in regression and classification problems.

Suggested Citation

  • Takayuki Okuno & Akiko Takeda, 2020. "Bilevel Optimization of Regularization Hyperparameters in Machine Learning," Springer Optimization and Its Applications, in: Stephan Dempe & Alain Zemkoho (ed.), Bilevel Optimization, chapter 0, pages 169-194, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-52119-6_6
    DOI: 10.1007/978-3-030-52119-6_6
    as

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