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• W3035590076 abstract "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." @default.
• W3035590076 created "2020-06-19" @default.
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• W3035590076 date "2023-04-15" @default.
• W3035590076 modified "2023-09-25" @default.
• W3035590076 title "Online Metric Algorithms with Untrusted Predictions" @default.
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• W3035590076 doi "https://doi.org/10.1145/3582689" @default.
• W3035590076 hasPublicationYear "2023" @default.
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