AutoMLの先行事例

過去のHPO(Hyperparameter Optimization)に関する先行事例をAutomated Machine Learningから持ってきました。

• King, R., Feng, C., Sutherland, A.: Statlog: comparison of classification algorithms on large
  real-world problems. Applied Artificial Intelligence an International Journal 9(3), 289–333
  (1995)
• Lee, D., Sugiyama, M., von Luxburg, U., Guyon, I., Garnett, R. (eds.): Proceedings of
  the 30th International Conference on Advances in Neural Information Processing Systems
  (NeurIPS’16) (2016)
• Michie, D., Spiegelhalter, D., Taylor, C., Campbell, J. (eds.): Machine Learning, Neural and
  Statistical Classification. Ellis Horwood (1994)
• Ripley, B.D.: Statistical aspects of neural networks. Networks and chaos—statistical and
  probabilistic aspects 50, 40–123 (1993)
• Escalante, H., Montes, M., Sucar, E.: Particle Swarm Model Selection. Journal of Machine
  Learning Research 10, 405–440 (2009)
• Mantovani, R., Horvath, T., Cerri, R., Vanschoren, J., Carvalho, A.: Hyper-Parameter Tuning
  of a Decision Tree Induction Algorithm. In: 2016 5th Brazilian Conference on Intelligent
  Systems (BRACIS). pp. 37–42. IEEE Computer Society Press (2016)
• Olson, R., La Cava, W.,Mustahsan, Z., Varik, A., Moore, J.: Data-driven advice for applying
  machine learning to bioinformatics problems. In: Proceedings of the Pacific Symposium in
  Biocomputing 2018. pp. 192–203 (2018)
• Sanders, S., Giraud-Carrier, C.: Informing the Use of Hyperparameter Optimization Through
  Metalearning. In: Gottumukkala, R., Ning, X., Dong, G., Raghavan, V., Aluru, S., Karypis,
  G., Miele, L., Wu, X. (eds.) 2017 IEEE International Conference on Big Data (Big Data).
  IEEE Computer Society Press (2017)
• Thornton, C., Hutter, F., Hoos, H., Leyton-Brown, K.: Auto-WEKA: combined selection and
  hyperparameter optimization of classification algorithms. In: Dhillon, I., Koren, Y., Ghani,
  R., Senator, T., Bradley, P., Parekh, R., He, J., Grossman, R., Uthurusamy, R. (eds.) The
  19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
  (KDD’13). pp. 847–855. ACM Press (2013)
• 1. Golovin, D., Solnik, B., Moitra, S., Kochanski, G., Karro, J., Sculley, D.: Google Vizier: A
     service for black-box optimization. In: Matwin, S., Yu, S., Farooq, F. (eds.) Proceedings of
     the 23rd ACM SIGKDD International Conference on Knowledge Discovery and DataMining
     (KDD). pp. 1487–1495. ACM Press (2017)

ここからはサービスの事例

• Amazon: Automatic model tuning (2018), https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning.html
• Li, F.F., Li, J.: Cloud AutoML: Making AI accessible to every business (2018), https://www.blog.google/products/google-cloud/cloud-automl-making-ai-accessible-every-business/
• SIGOPT: Improve ML models 100x faster (2018), https://sigopt.com/

何か発表する時の裏取りに使おう。