Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees

national conference on artificial intelligence, 2020.

Cited by: 22|Bibtex|Views37
Other Links: academic.microsoft.com|arxiv.org

Abstract:

Algorithms typically come with tunable parameters that have a considerable impact on the computational resources they consume. Too often, practitioners must hand-tune the parameters, a tedious and error-prone task. A recent line of research provides algorithms that return nearly-optimal parameters from within a finite set. These algorit...More

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