# Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees

## TL;DR

This paper introduces a novel algorithm that learns a finite set of promising parameters from an infinite set, enabling efficient resource optimization with generalization guarantees in configuration problems.

## Contribution

It presents a new method for learning promising parameters within infinite sets, addressing limitations of existing discretization approaches.

## Key findings

- The algorithm effectively identifies near-optimal parameters in infinite spaces.
- It applies to various domains with piecewise constant performance functions.
- Provides theoretical guarantees for generalization and resource efficiency.

## 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.