Mistake-bounded online learning with operation caps
Machine Learning
2025-09-05 v1 Computational Complexity
Discrete Mathematics
Abstract
We investigate the mistake-bound model of online learning with caps on the number of arithmetic operations per round. We prove general bounds on the minimum number of arithmetic operations per round that are necessary to learn an arbitrary family of functions with finitely many mistakes. We solve a problem on agnostic mistake-bounded online learning with bandit feedback from (Filmus et al, 2024) and (Geneson \& Tang, 2024). We also extend this result to the setting of operation caps.
Keywords
Cite
@article{arxiv.2509.03892,
title = {Mistake-bounded online learning with operation caps},
author = {Jesse Geneson and Meien Li and Linus Tang},
journal= {arXiv preprint arXiv:2509.03892},
year = {2025}
}