Utilitarian Algorithm Configuration for Infinite Parameter Spaces
Devon R. Graham, Kevin Leyton-Brown
摘要
Utilitarian algorithm configuration is a general-purpose technique for automatically searching the parameter space of a given algorithm to optimize its performance, as measured by a given utility function, on a given set of inputs. Recently introduced utilitarian configuration procedures offer optimality guarantees about the returned parameterization while provably adapting to the hardness of the underlying problem. However, the applicability of these approaches is severely limited by the fact that they only search a finite, relatively small set of parameters. They cannot effectively search the configuration space of algorithms with continuous or uncountable parameters. In this paper we introduce a new procedure, which we dub COUP (Continuous, Optimistic Utilitarian Procrastination). COUP is designed to search infinite parameter spaces efficiently to find good configurations quickly. Furthermore, COUP maintains the theoretical benefits of previous utilitarian configuration procedures when applied to finite parameter spaces but is significantly faster, both provably and experimentally.
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- ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite PoolGellért Weisz, András György, Wei-I Lin, Devon R. Graham 等NeurIPS 2020 · 被引用 7 次
- AC-Band: A Combinatorial Bandit-Based Approach to Algorithm ConfigurationJasmin Brandt, Elias Schede, Björn Haddenhorst, Viktor Bengs 等AAAI 2023 · 被引用 7 次
- Formalizing Preferences Over Runtime DistributionsDevon R. Graham, Kevin Leyton-Brown, Tim RoughgardenICML 2023 · 被引用 6 次
- How much data is sufficient to learn high-performing algorithms? generalization guarantees for data-driven algorithm designMaria-Florina Balcan, Dan F. DeBlasio, Travis Dick, Carl Kingsford 等STOC 2021 · 被引用 3 次
- Utilitarian Algorithm ConfigurationDevon R. Graham, Kevin Leyton-Brown, Tim RoughgardenNeurIPS 2023 · 被引用 2 次
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