Parsimonious Learning-Augmented Online Metric Matching

ArXi:2605.26886v1 Announce Type: cross Learning-augmented algorithms have received significant attention in recent years, particularly in the context of online optimization. Motivated by the high computational cost of generating predictions, a growing line of work studies the tradeoff between performance guarantees and the number of predictions used in learning-augmented algorithms for problems such as caching and metrical task systems.