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The classical 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. Often this is not an issue with machine learning approaches, which 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 them into account. In particular, we study the following online selection problems: (i) the classical 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 classical 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.
Author Information
Antonios Antoniadis (University of Twente)
Themis Gouleakis (Max Planck Institute for Informatics)
Pieter Kleer (Max Planck Institute for Informatics)
Pavel Kolev (Max-Planck-Institut für Informatik)
More from the Same Authors
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2019 Poster: Distribution-Independent PAC Learning of Halfspaces with Massart Noise »
Ilias Diakonikolas · Themis Gouleakis · Christos Tzamos -
2019 Oral: Distribution-Independent PAC Learning of Halfspaces with Massart Noise »
Ilias Diakonikolas · Themis Gouleakis · Christos Tzamos -
2017 Poster: Approximation Algorithms for $\ell_0$-Low Rank Approximation »
Karl Bringmann · Pavel Kolev · David Woodruff