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)
- 45. 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。