2020/03/04 by Antonios P. Antoniadis, Christian Coester, Antoniadis, Antonios +7 · 8 citations
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Optimization and Search Problems #Stochastic Gradient Optimization Techniques
paper · doi:10.48550/arxiv.2003.02144
openalex publication_date 2023/04/01 · openalex created_date 2025/11/01 · openalex updated_date 2026/07/16
Machine-learned predictors, although achieving very good results for inputs resembling training data, cannot possibly provide perfect predictions in all situations. Still, decision-making systems that are based on such predictors need not only benefit from good predictions, but should also achieve a decent performance when the predictions are inadequate. In this article, we propose a prediction setup for arbitrary metrical task systems (MTS) (e.g., caching, k-server, and convex body chasing) and online matching on the line. We utilize results from the theory of online algorithms to show how to make the setup robust. Specifically, for caching, we present an algorithm whose performance, as a function of the prediction error, is exponentially better than what is achievable for general MTS. Finally, we present an empirical evaluation of our methods on real-world datasets, which suggests practicality.