Budget Allocation for Unknown Value Functions in a Lipschitz Space
Machine Learning
2025-10-21 v2
Abstract
Building learning models frequently requires evaluating numerous intermediate models. Examples include models considered during feature selection, model structure search, and parameter tunings. The evaluation of an intermediate model influences subsequent model exploration decisions. Although prior knowledge can provide initial quality estimates, true performance is only revealed after evaluation. In this work, we address the challenge of optimally allocating a bounded budget to explore the space of intermediate models. We formalize this as a general budget allocation problem over unknown-value functions within a Lipschitz space.
Keywords
Cite
@article{arxiv.2510.10605,
title = {Budget Allocation for Unknown Value Functions in a Lipschitz Space},
author = {MohammadHossein Bateni and Hossein Esfandiari and Samira HosseinGhorban and Alireza Mirrokni and Radin Shahdaei},
journal= {arXiv preprint arXiv:2510.10605},
year = {2025}
}