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In-Context Freeze-Thaw Bayesian Optimization for Hyperparameter Optimization
April 26, 2024, 4:42 a.m. | Herilalaina Rakotoarison, Steven Adriaensen, Neeratyoy Mallik, Samir Garibov, Edward Bergman, Frank Hutter
cs.LG updates on arXiv.org arxiv.org
Abstract: With the increasing computational costs associated with deep learning, automated hyperparameter optimization methods, strongly relying on black-box Bayesian optimization (BO), face limitations. Freeze-thaw BO offers a promising grey-box alternative, strategically allocating scarce resources incrementally to different configurations. However, the frequent surrogate model updates inherent to this approach pose challenges for existing methods, requiring retraining or fine-tuning their neural network surrogates online, introducing overhead, instability, and hyper-hyperparameters. In this work, we propose FT-PFN, a novel surrogate …
abstract alternative arxiv automated bayesian box computational context costs cs.lg deep learning face however hyperparameter limitations optimization resources type updates
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