Online metric algorithms with untrusted predictions

Conference item

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 to benefit from good predictions but also to achieve a decent performance when the predictions are inadequate. In this paper, 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.

Authors: Antoniadis, A; Coester, C; Eliáš, M; Polak, A; Simon, B

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Journal of Machine Learning Research
• Pages: 322-332
• Series: Proceedings of Machine Learning Research
• Series number: 119
• Publication date: 2020-11-21
• Event title: 37th International Conference on Machine Learning (ICML 2020)
• Event location: Virtual event
• Event website: https://icml.cc/Conferences/2020
• Event start date: 2020-07-13
• Event end date: 2020-07-18
• ISSN: 2640-3498
• ISBN: 9781713821120
• Language: English
• Keywords: FFR