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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees

February 1, 2023

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Authors

Maria-Florina Balcan

Carnegie Mellon University


Tuomas Sandholm

Carnegie Mellon University


Ellen Vitercik

Carnegie Mellon University


DOI:

10.1609/aaai.v34i04.5721


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 algorithms can be used when the parameter space is infinite by providing as input a random sample of parameters. This data-independent discretization, however, might miss pockets of nearly-optimal parameters: prior research has presented scenarios where the only viable parameters lie within an arbitrarily small region. We provide an algorithm that learns a finite set of promising parameters from within an infinite set. Our algorithm can help compile a configuration portfolio, or it can be used to select the input to a configuration algorithm for finite parameter spaces. Our approach applies to any configuration problem that satisfies a simple yet ubiquitous structure: the algorithm's performance is a piecewise constant function of its parameters. Prior research has exhibited this structure in domains from integer programming to clustering.

Topics: AAAI

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HOW TO CITE:

Maria-Florina Balcan||Tuomas Sandholm||Ellen Vitercik Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees Proceedings of the AAAI Conference on Artificial Intelligence (2020) 3227-3234.

Maria-Florina Balcan||Tuomas Sandholm||Ellen Vitercik Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees AAAI 2020, 3227-3234.

Maria-Florina Balcan||Tuomas Sandholm||Ellen Vitercik (2020). Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees. Proceedings of the AAAI Conference on Artificial Intelligence, 3227-3234.

Maria-Florina Balcan||Tuomas Sandholm||Ellen Vitercik. Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.3227-3234.

Maria-Florina Balcan||Tuomas Sandholm||Ellen Vitercik. 2020. Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees. "Proceedings of the AAAI Conference on Artificial Intelligence". 3227-3234.

Maria-Florina Balcan||Tuomas Sandholm||Ellen Vitercik. (2020) "Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees", Proceedings of the AAAI Conference on Artificial Intelligence, p.3227-3234

Maria-Florina Balcan||Tuomas Sandholm||Ellen Vitercik, "Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees", AAAI, p.3227-3234, 2020.

Maria-Florina Balcan||Tuomas Sandholm||Ellen Vitercik. "Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.3227-3234.

Maria-Florina Balcan||Tuomas Sandholm||Ellen Vitercik. "Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 3227-3234.

Maria-Florina Balcan||Tuomas Sandholm||Ellen Vitercik. Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees. AAAI[Internet]. 2020[cited 2023]; 3227-3234.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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