@inproceedings{cb3a5b2f2b0c4292a1434cfa9aee7128,
title = "Discrete-Smoothness in Online Algorithms with Predictions",
abstract = "In recent years, there has been an increasing focus on designing online algorithms with (machine-learned) predictions. The ideal learning-augmented algorithm is comparable to the optimum when given perfect predictions (consistency), to the best online approximation for arbitrary predictions (robustness), and should interpolate between these extremes as a smooth function of the prediction error. In this paper, we quantify these guarantees in terms of a general property that we call discrete-smoothness and achieve discrete-smooth algorithms for online covering, specifically the facility location and set cover problems. For set cover, our work improves the results of Bamas, Maggiori, and Svensson (2020) by augmenting consistency and robustness with smoothness guarantees. For facility location, our work improves on prior work by Almanza et al. (2021) by generalizing to nonuniform costs and also providing smoothness guarantees by augmenting consistency and robustness.",
author = "Yossi Azar and Debmalya Panigrahi and Noam Touitou",
note = "Publisher Copyright: {\textcopyright} 2023 Neural information processing systems foundation. All rights reserved.; 37th Conference on Neural Information Processing Systems, NeurIPS 2023 ; Conference date: 10-12-2023 Through 16-12-2023",
year = "2023",
language = "الإنجليزيّة",
series = "Advances in Neural Information Processing Systems",
publisher = "Neural information processing systems foundation",
editor = "A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine",
booktitle = "Advances in Neural Information Processing Systems 36 - 37th Conference on Neural Information Processing Systems, NeurIPS 2023",
address = "الولايات المتّحدة",
}