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Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning. (arXiv:2007.04074v3 [cs.LG] UPDATED)
Oct. 5, 2022, 1:14 a.m. | Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Lindauer, Frank Hutter
stat.ML updates on arXiv.org arxiv.org
Automated Machine Learning (AutoML) supports practitioners and researchers
with the tedious task of designing machine learning pipelines and has recently
achieved substantial success. In this paper, we introduce new AutoML approaches
motivated by our winning submission to the second ChaLearn AutoML challenge. We
develop PoSH Auto-sklearn, which enables AutoML systems to work well on large
datasets under rigid time limits by using a new, simple and meta-feature-free
meta-learning technique and by employing a successful bandit strategy for
budget allocation. However, …
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