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Efficient End-to-End AutoML via Scalable Search Space Decomposition. (arXiv:2206.09423v2 [cs.LG] UPDATED)
June 27, 2022, 1:11 a.m. | Yang Li, Yu Shen, Wentao Zhang, Ce Zhang, Bin Cui
cs.LG updates on arXiv.org arxiv.org
End-to-end AutoML has attracted intensive interests from both academia and
industry which automatically searches for ML pipelines in a space induced by
feature engineering, algorithm/model selection, and hyper-parameter tuning.
Existing AutoML systems, however, suffer from scalability issues when applying
to application domains with large, high-dimensional search spaces. We present
VolcanoML, a scalable and extensible framework that facilitates systematic
exploration of large AutoML search spaces. VolcanoML introduces and implements
basic building blocks that decompose a large search space into smaller ones, …
More from arxiv.org / cs.LG updates on arXiv.org
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