# Utilitarian Algorithm Configuration for Infinite Parameter Spaces

arXiv: https://arxiv.org/abs/2405.18246 · HTML: https://mlsanity.com/paper/2405.18246/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 28 May 2024.

## Builds on (most cited first, 9 of 9)

- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Non-stochastic Best Arm Identification and Hyperparameter Optimization](https://mlsanity.com/paper/1502.07943/) (2015)
- [A PAC Approach to Application-Specific Algorithm Selection](https://mlsanity.com/paper/1511.07147/) (2015)
- [Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems](https://mlsanity.com/paper/1611.04535/) (2016)
- [LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration](https://mlsanity.com/paper/1807.00755/) (2018)
- [Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration](https://mlsanity.com/paper/1902.05454/) (2019)
- [AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration](https://mlsanity.com/paper/2212.00333/) (2022)
- [Formalizing Preferences Over Runtime Distributions](https://mlsanity.com/paper/2205.13028/) (2022)
- [Utilitarian Algorithm Configuration](https://mlsanity.com/paper/2310.20401/) (2023)

## Built on by (most cited first, 2 of 2)

- [QuickScope: Certifying Hard Questions in Dynamic LLM Benchmarks](https://mlsanity.com/paper/2604.17842/) (2026)
- [Practical, Utilitarian Algorithm Configuration](https://mlsanity.com/paper/2510.14683/) (2025)

## Abstract (arXiv)

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.