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ブラックボックス最適化とその応用

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『CCSE2019』で発表された資料です。

https://ccse.jp/2019/

Published in: Engineering
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ブラックボックス最適化とその応用

  1. 1. Copyright © GREE, Inc. All Rights Reserved.
  2. 2. Copyright © GREE, Inc. All Rights Reserved. • 
 AI • 
 • • Automated Machine Learning (AutoML) https://y0z.github.io/about/
  3. 3. Copyright © GREE, Inc. All Rights Reserved. • 
 
 ! 
 • ! • • Minimize f(x) subject to x ∈ X f(x)
  4. 4. Copyright © GREE, Inc. All Rights Reserved. • • ! ( ) ! • • AutoML 1 (Feurer and Hutter, 2019) • ! ! f(x) f(x) f(x) x ” ”
  5. 5. Copyright © GREE, Inc. All Rights Reserved. • • ! • • • • • • • Grey-box Bayesian Optimization for AutoML
 https://slideslive.com/38916582/keynote-greybox-bayesian- optimization-for-automl f(x)
  6. 6. Copyright © GREE, Inc. All Rights Reserved. • 
 GP-EI SMAC TPE • 
 Population-based methods CMA-ES • 
 Nelder–Mead MADS • • Google Vizier (Google) • Optuna (PFN) • Nevergrad (Facebook)
  7. 7. Copyright © GREE, Inc. All Rights Reserved. • 
 GP-EI SMAC TPE • 
 Population-based methods CMA-ES • 
 Nelder–Mead MADS • • Google Vizier (Google) • Optuna (PFN) • Nevergrad (Facebook)
  8. 8. Copyright © GREE, Inc. All Rights Reserved. • • (Cohen et al., 2005; Ozaki et al., 2017) Nelder–Mead Nelder and Mead, 1965 CNN (Ozaki et al., 2017)
  9. 9. Copyright © GREE, Inc. All Rights Reserved. Nelder–Mead reflect, expand, inside contract, outside contract, shrink 5 reflect, expand, inside contract, outside contract shrink
  10. 10. Copyright © GREE, Inc. All Rights Reserved. • Nelder–Mead • Nelder–Mead Nelder–Mead Accelerating the Nelder–Mead Method with Predictive Parallel Evaluation Yoshihiko Ozaki, Shuhei Watanabe, and Masaki Onishi
 6th ICML Workshop on Automated Machine Learning, Jun 2019. ! !f(x) ∼ GP(m(x), k(x, x′)) g(x)
  11. 11. Copyright © GREE, Inc. All Rights Reserved. 1. 2. Nelder–Mead 3. P 4. 2. Nelder–Mead ! !f(x) ∼ GP(m(x), k(x, x′)) g(x)
  12. 12. Copyright © GREE, Inc. All Rights Reserved. • • 6 (Klein et al., 2018) • ! ! • Baseline 1 shrink ( ) • Baseline 2 • • Baseline 1 49% 2 13% P = 10 J = 1,2,3,4,5 Nelder–Mead Method J Average # of eval steps Average # of evaluations Baseline 1 - 590.27 (±141.42) 614.10 (±142.82) Baseline 2 - 347.27 (±89.32) 3469.67 (±893.21) Proposed 1 406.20 (±97.24) 1534.20 (±427.69) 2 314.13 (±72.26) 2307.83 (±558.02) 3 304.97 (±54.57) 2679.13 (±464.80) 4 310.60 (±67.58) 2948.20 (±642.62) 5 301.90 (±58.70) 2942.33 (±567.27)
  13. 13. Copyright © GREE, Inc. All Rights Reserved. • ! ! • ! P = 10,20,30,40 J = 1,2,3,4,5 P, J Nelder–Mead
  14. 14. Copyright © GREE, Inc. All Rights Reserved. • • • • 8 5 KDD AutoML Workshop • Yoshihiko Ozaki and Masaki Onishi,
 “Practical Deep Neural Network Performance Prediction for Hyperparameter Optimization,”
 To appear. • https://sites.google.com/view/automl2019-workshop/
  15. 15. Copyright © GREE, Inc. All Rights Reserved.

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