License: 
Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ISAAC.2021.69
URN: urn:nbn:de:0030-drops-155029
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2021/15502/
Tang, Shaojie ;
Yuan, Jing
Adaptive Regularized Submodular Maximization
Abstract
In this paper, we study the problem of maximizing the difference between an adaptive submodular (revenue) function and a non-negative modular (cost) function. The input of our problem is a set of n items, where each item has a particular state drawn from some known prior distribution The revenue function g is defined over items and states, and the cost function c is defined over items, i.e., each item has a fixed cost. The state of each item is unknown initially and one must select an item in order to observe its realized state. A policy π specifies which item to pick next based on the observations made so far. Denote by g_{avg}(π) the expected revenue of π and let c_{avg}(π) denote the expected cost of π. Our objective is to identify the best policy π^o ∈ arg max_π g_{avg}(π)-c_{avg}(π) under a k-cardinality constraint. Since our objective function can take on both negative and positive values, the existing results of submodular maximization may not be applicable. To overcome this challenge, we develop a series of effective solutions with performance guarantees. Let π^o denote the optimal policy. For the case when g is adaptive monotone and adaptive submodular, we develop an effective policy π^l such that g_{avg}(π^l) - c_{avg}(π^l) ≥ (1-1/e-ε)g_{avg}(π^o) - c_{avg}(π^o), using only O(nε^{-2}log ε^{-1}) value oracle queries. For the case when g is adaptive submodular, we present a randomized policy π^r such that g_{avg}(π^r) - c_{avg}(π^r) ≥ 1/eg_{avg}(π^o) - c_{avg}(π^o).
BibTeX - Entry
@InProceedings{tang_et_al:LIPIcs.ISAAC.2021.69,
author = {Tang, Shaojie and Yuan, Jing},
title = {{Adaptive Regularized Submodular Maximization}},
booktitle = {32nd International Symposium on Algorithms and Computation (ISAAC 2021)},
pages = {69:1--69:13},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-214-3},
ISSN = {1868-8969},
year = {2021},
volume = {212},
editor = {Ahn, Hee-Kap and Sadakane, Kunihiko},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2021/15502},
URN = {urn:nbn:de:0030-drops-155029},
doi = {10.4230/LIPIcs.ISAAC.2021.69},
annote = {Keywords: Adaptive submodularity, approximation algorithms, active learning}
}
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Keywords: |
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Adaptive submodularity, approximation algorithms, active learning |
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Collection: |
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32nd International Symposium on Algorithms and Computation (ISAAC 2021) |
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Issue Date: |
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2021 |
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Date of publication: |
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30.11.2021 |
