On Taking Advantage of Opportunistic Meta-knowledge to Reduce Configuration Spaces for Automated Machine Learning. (arXiv:2208.04376v1 [cs.LG])

The automated machine learning (AutoML) process can require searching through
complex configuration spaces of not only machine learning (ML) components and
their hyperparameters but also ways of composing them together, i.e. forming ML
pipelines. Optimisation efficiency and the model accuracy attainable for a
fixed time budget suffer if this pipeline configuration space is excessively
large. A key research question is whether it is both possible and practical to
preemptively avoid costly evaluations of poorly performing ML pipelines by
leveraging their …

arxiv automated machine learning knowledge learning lg machine machine learning meta reduce