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Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2019: Machine Learning and Knowledge Discovery in Databases pp 44–50Cite as

HyperUCB: Hyperparameter Optimization Using Contextual Bandits

Part of the Communications in Computer and Information Science book series (CCIS,volume 1167)

Abstract

Setting the optimal hyperparameters of a learning algorithm is a crucial task. Common approaches such as a grid search over the hyperparameter space or randomly sampling hyperparameters require many configurations to be evaluated in order to perform well. Hence, they either yield suboptimal hyperparameter configurations or are expensive in terms of computational resources. As a remedy, Hyperband, an exploratory bandit-based algorithm, introduces an early-stopping strategy to quickly provide competitive configurations given a resource budget which often outperforms Bayesian optimization approaches. However, Hyperband keeps sampling iid configurations for assessment without taking previous evaluations into account. We propose HyperUCB, a UCB extension of Hyperband which assesses the sampled configurations and only evaluates promising samples. We compare our approach on MNIST data against Hyperband and show that we perform better in most cases.

Keywords

  • Hyperparameter optimization
  • Multi-armed bandits

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References

  1. Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47(2–3), 235–256 (2002). https://doi.org/10.1023/A:1013689704352

    CrossRef  MATH  Google Scholar 

  2. Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(Feb), 281–305 (2012)

    MathSciNet  MATH  Google Scholar 

  3. Bergstra, J.S., Bardenet, R., Bengio, Y., Kégl, B.: Algorithms for hyper-parameter optimization. In: Advances in Neural Information Processing Systems (2011)

    Google Scholar 

  4. Falkner, S., Klein, A., Hutter, F.: BOHB: robust and efficient hyperparameter optimization at scale. In: International Conference on Machine Learning (2018)

    Google Scholar 

  5. Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 507–523. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25566-3_40

    CrossRef  Google Scholar 

  6. Jamieson, K., Talwalkar, A.: Non-stochastic best arm identification and hyperparameter optimization. In: Artificial Intelligence and Statistics (2016)

    Google Scholar 

  7. Li, L., Chu, W., Langford, J., Schapire, R.E.: A contextual-bandit approach to personalized news article recommendation. In: Proceedings of the 19th International Conference on World Wide Web (2010)

    Google Scholar 

  8. Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., Talwalkar, A.: Hyperband: a novel bandit-based approach to hyperparameter optimization. J. Mach. Learn. Res. 18, 6765–6816 (2018)

    Google Scholar 

  9. Shang, X., Kaufmann, E., Valko, M.: A simple dynamic bandit algorithm for hyper-parameter tuning. In: Workshop on Automated Machine Learning at International Conference on Machine Learning (2019)

    Google Scholar 

  10. Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Advances in Neural Information Processing Systems (2012)

    Google Scholar 

  11. Sparks, E.R., Talwalkar, A., Haas, D., Franklin, M.J., Jordan, M.I., Kraska, T.: Automating model search for large scale machine learning. In: Proceedings of the Sixth ACM Symposium on Cloud Computing (2015)

    Google Scholar 

  12. Tavakol, M., Brefeld, U.: A unified contextual bandit framework for long- and short-term recommendations. In: Ceci, M., Hollmén, J., Todorovski, L., Vens, C., Džeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10535, pp. 269–284. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71246-8_17

    CrossRef  Google Scholar 

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Authors and Affiliations

  1. Technical University of Dortmund, Dortmund, Germany

    Maryam Tavakol & Katharina Morik

  2. Leuphana University of Lüneburg, Lüneburg, Germany

    Sebastian Mair

Authors
  1. Maryam Tavakol
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  2. Sebastian Mair
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  3. Katharina Morik
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Corresponding author

Correspondence to Maryam Tavakol .

Editor information

Editors and Affiliations

  1. Institut National des Sciences Appliquées, Rennes, France

    Peggy Cellier

  2. Maastricht University, Maastricht, The Netherlands

    Kurt Driessens

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Tavakol, M., Mair, S., Morik, K. (2020). HyperUCB: Hyperparameter Optimization Using Contextual Bandits. In: Cellier, P., Driessens, K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019. Communications in Computer and Information Science, vol 1167. Springer, Cham. https://doi.org/10.1007/978-3-030-43823-4_4

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  • DOI: https://doi.org/10.1007/978-3-030-43823-4_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-43822-7

  • Online ISBN: 978-3-030-43823-4

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