• Corpus ID: 235436202

# Improved Regret Bounds for Online Submodular Maximization

@article{Sadeghi2021ImprovedRB,
title={Improved Regret Bounds for Online Submodular Maximization},
author={Omid Sadeghi and Prasanna Sanjay Raut and Maryam Fazel},
journal={ArXiv},
year={2021},
volume={abs/2106.07836}
}
• Published 15 June 2021
• Computer Science, Mathematics
• ArXiv
In this paper, we consider an online optimization problem over T rounds where at each step t ∈ [T ], the algorithm chooses an action xt from the fixed convex and compact domain set K. A utility function ft(·) is then revealed and the algorithm receives the payoff ft(xt). This problem has been previously studied under the assumption that the utilities are adversarially chosen monotone DR-submodular functions and O( √ T ) regret bounds have been derived. We first characterize the class of…
2 Citations

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