Online metric algorithms with untrusted predictions

Antonios Antoniadis, Christian Coester, Marek Elias, Adam Polak, Bertrand Simon
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:345-355, 2020.

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 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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v119-antoniadis20a, title = {Online metric algorithms with untrusted predictions}, author = {Antoniadis, Antonios and Coester, Christian and Elias, Marek and Polak, Adam and Simon, Bertrand}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {345--355}, year = {2020}, editor = {Hal Daumé III and Aarti Singh}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/antoniadis20a/antoniadis20a.pdf}, url = { http://proceedings.mlr.press/v119/antoniadis20a.html }, 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 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.} }
Endnote
%0 Conference Paper %T Online metric algorithms with untrusted predictions %A Antonios Antoniadis %A Christian Coester %A Marek Elias %A Adam Polak %A Bertrand Simon %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-antoniadis20a %I PMLR %P 345--355 %U http://proceedings.mlr.press/v119/antoniadis20a.html %V 119 %X 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.
APA
Antoniadis, A., Coester, C., Elias, M., Polak, A. & Simon, B.. (2020). Online metric algorithms with untrusted predictions. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:345-355 Available from http://proceedings.mlr.press/v119/antoniadis20a.html .

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