Efficient End-to-End AutoML via Scalable Search Space Decomposition

@article{Li2022EfficientEA,
  title={Efficient End-to-End AutoML via Scalable Search Space Decomposition},
  author={Yang Li and Yu Shen and Wentao Zhang and Ce Zhang and Bin Cui},
  journal={ArXiv},
  year={2022},
  volume={abs/2206.09423}
}
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 explo-ration of large AutoML search… 

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