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Found 4 papers, 2 papers with code
Learning meta-features for AutoML
This paper tackles the AutoML problem, aimed to automatically select an ML algorithm and its hyper-parameter configuration most appropriate to the dataset at hand.
Black-Box Optimization Revisited: Improving Algorithm Selection Wizards through Massive Benchmarking
We demonstrate the advantages of such a broad collection by deriving from it Automated Black Box Optimizer (ABBO), a general-purpose algorithm selection wizard.
Distribution-Based Invariant Deep Networks for Learning Meta-Features
On both tasks, Dida learns meta-features supporting the characterization of a (labelled) dataset.
Automated Machine Learning with Monte-Carlo Tree Search
The AutoML task consists of selecting the proper algorithm in a machine learning portfolio, and its hyperparameter values, in order to deliver the best performance on the dataset at hand.
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