Spectral bandits achieve scalable regret in graph-structured recommendation by using an effective dimension to learn good policies from few node evaluations.
https://arxiv.org/abs/1409.8428 Nonstochastic multi-armed bandits with graph-structured feedback
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A new algorithm for online influence maximization under a total budget constraint using the independent cascade model and edge-level semi-bandit feedback, with improved regret bounds for both budgeted and cardinality settings.
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Spectral bandits
Spectral bandits achieve scalable regret in graph-structured recommendation by using an effective dimension to learn good policies from few node evaluations.
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Budgeted Online Influence Maximization
A new algorithm for online influence maximization under a total budget constraint using the independent cascade model and edge-level semi-bandit feedback, with improved regret bounds for both budgeted and cardinality settings.