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Automated hyperparameter optimization (HPO) has gained great popularity and is an important ingredient of most automated machine learning frameworks.
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L. Kotthoff, C. Thornton, H. H. Hoos, F. Hutter, and K. Leyton-Brown, “Auto-weka: Automatic model selection and hyperparameter optimization in weka,” in Automated Machine Learning: Methods, Systems, Challenges , F. Hutter, L. Kotthoff, and J. Vanschoren, Eds. Cham: Springer International Publishing, 2019, pp. 81–95
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M. Lindauer, M. Feurer, K. Eggensperger, A. Biedenkapp, and F. Hutter, “Towards assessing the impact of bayesian optimization’s own hyperparameters,” 2019
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2021
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Closest in time.
L. Zimmer, M. Lindauer, and F. Hutter, “Auto-pytorch tabular: Multi-fidelity metalearning for efficient and robust autodl,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 43, no. 9, pp. 3079 – 3090, 2021
2021
Closest in time.