Secretary and online matching problems with machine learned advice
Antoniadis,Antonios Gouleakis,Themis Kleer,Pieter Kolev,Pavel
Antoniadis,Antonios
Gouleakis,Themis
Kleer,Pieter
Kolev,Pavel
Abstract
The classic analysis of online algorithms, due to its worst-case nature, can be quite pessimistic when the input instance at hand is far from worst-case. In contrast, machine learning approaches shine in exploiting patterns in past inputs in order to predict the future. However, such predictions, although usually accurate, can be arbitrarily poor. Inspired by a recent line of work, we augment three well-known online settings with machine learned predictions about the future, and develop algorithms that take these predictions into account. In particular, we study the following online selection problems: (i) the classic secretary problem, (ii) online bipartite matching and (iii) the graphic matroid secretary problem. Our algorithms still come with a worst-case performance guarantee in the case that predictions are subpar while obtaining an improved competitive ratio (over the best-known classic online algorithm for each problem) when the predictions are sufficiently accurate. For each algorithm, we establish a trade-off between the competitive ratios obtained in the two respective cases.
Description
Publisher Copyright: © 2023 The Author(s)
Date
2023-05
Journal Title
Journal ISSN
Volume Title
Publisher
Research Projects
Organizational Units
Journal Issue
Keywords
Learning augmentation, Machine learned advice, Online bipartite matching, Secretary problem
Citation
Antoniadis, A, Gouleakis, T, Kleer, P & Kolev, P 2023, 'Secretary and online matching problems with machine learned advice', Discrete Optimization, vol. 48, no. part 2, 100778. https://doi.org/10.1016/j.disopt.2023.100778
License
info:eu-repo/semantics/openAccess