『CCSE2019』で発表された資料です。 https://ccse.jp/2019/

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AI
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• Automated Machine Learning (AutoML)
https://y0z.github.io/about/

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!
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Minimize f(x)
subject to x ∈ X
f(x)

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• ! ( )
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• AutoML 1 (Feurer and Hutter, 2019)
• ! !
f(x)
f(x)
f(x) x
” ”

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• Grey-box Bayesian Optimization for AutoML
https://slideslive.com/38916582/keynote-greybox-bayesian-
optimization-for-automl
f(x)

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GP-EI SMAC TPE
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Population-based methods CMA-ES
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Nelder–Mead MADS
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• Google Vizier (Google)
• Optuna (PFN)
• Nevergrad (Facebook)

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•
GP-EI SMAC TPE
•
Population-based methods CMA-ES
•
Nelder–Mead MADS
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• Google Vizier (Google)
• Optuna (PFN)
• Nevergrad (Facebook)

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(Cohen et al., 2005; Ozaki et al., 2017)
Nelder–Mead
Nelder and Mead, 1965
CNN (Ozaki et al., 2017)

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Nelder–Mead
reflect, expand, inside contract, outside contract, shrink 5
reflect, expand, inside contract, outside contract shrink

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• 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)

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1.
2. Nelder–Mead
3. P
4. 2.
Nelder–Mead
! !f(x) ∼ GP(m(x), k(x, x′)) g(x)

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• 6 (Klein et
al., 2018)
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• Baseline 1 shrink ( )
• Baseline 2
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• 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)

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• ! !
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P = 10,20,30,40 J = 1,2,3,4,5
P, J
Nelder–Mead

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• 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/

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