1. Logarithmic Regret for Unconstrained Submodular Maximization Stochastic Bandit
- Author
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Zhou, Julien, Gaillard, Pierre, Rahier, Thibaud, and Arbel, Julyan
- Subjects
Computer Science - Machine Learning ,Mathematics - Combinatorics ,Mathematics - Optimization and Control ,Statistics - Machine Learning - Abstract
We address the online unconstrained submodular maximization problem (Online USM), in a setting with stochastic bandit feedback. In this framework, a decision-maker receives noisy rewards from a nonmonotone submodular function, taking values in a known bounded interval. This paper proposes Double-Greedy - Explore-then-Commit (DG-ETC), adapting the Double-Greedy approach from the offline and online full-information settings. DG-ETC satisfies a O(d log(dT)) problemdependent upper bound for the 1/2-approximate pseudo-regret, as well as a O(dT^{2/3}log(dT)^{1/3}) problem-free one at the same time, outperforming existing approaches. To that end, we introduce a notion of hardness for submodular functions, characterizing how difficult it is to maximize them with this type of strategy.
- Published
- 2024