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- W4319049573 abstract "Hyperparameter optimization (HPO) is concerned with the automated search for the most appropriate hyperparameter configuration (HPC) of a parameterized machine learning algorithm. A state-of-the-art HPO method is Hyperband, which, however, has its own parameters that influence its performance. One of these parameters, the maximal budget, is especially problematic: If chosen too small, the budget needs to be increased in hindsight and, as Hyperband is not incremental by design, the entire algorithm must be re-run. This is not only costly but also comes with a loss of valuable knowledge already accumulated. In this paper, we propose incremental variants of Hyperband that eliminate these drawbacks, and show that these variants satisfy theoretical guarantees qualitatively similar to those for the original Hyperband with the right budget. Moreover, we demonstrate their practical utility in experiments with benchmark data sets." @default.
- W4319049573 created "2023-02-04" @default.
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- W4319049573 date "2023-02-01" @default.
- W4319049573 modified "2023-10-14" @default.
- W4319049573 title "Iterative Deepening Hyperband" @default.
- W4319049573 doi "https://doi.org/10.48550/arxiv.2302.00511" @default.
- W4319049573 hasPublicationYear "2023" @default.
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