Summary of Utilitarian Algorithm Configuration For Infinite Parameter Spaces, by Devon Graham and Kevin Leyton-brown
Utilitarian Algorithm Configuration for Infinite Parameter Spaces
by Devon Graham, Kevin Leyton-Brown
First submitted to arxiv on: 28 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce a new algorithm configuration technique called COUP (Continuous, Optimistic Utilitarian Procrastination) that optimizes performance on a given set of inputs. The method offers guarantees about the returned parameterization while adapting to the hardness of the underlying problem. Unlike existing approaches, COUP can efficiently search infinite parameter spaces and find good configurations quickly. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This new algorithm is designed to solve problems with continuous or uncountable parameters. It maintains the theoretical benefits of previous utilitarian configuration procedures when applied to finite parameter spaces but is significantly faster. The paper introduces a novel approach that can effectively search the configuration space of algorithms, which has implications for various applications. |
