Hyperparameter Optimization

Author:

Feurer Matthias,Hutter Frank

Publisher

Springer International Publishing

Reference160 articles.

1. Proceedings of the International Conference on Learning Representations (ICLR’18) (2018), published online: iclr.cc

2. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015), https://www.tensorflow.org/

3. Ahmed, M., Shahriari, B., Schmidt, M.: Do we need “harmless” Bayesian optimization and “first-order” Bayesian optimization. In: NeurIPS Workshop on Bayesian Optimization (BayesOpt’16) (2016)

4. Alaa, A., van der Schaar, M.: AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning. In: Dy and Krause [27], pp. 139–148

5. Almeida, L.B., Langlois, T., Amaral, J.D., Plakhov, A.: Parameter Adaptation in Stochastic Optimization, p. 111–134. Cambridge University Press (1999)

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