Spectral bandit algorithms for graph-smooth payoffs achieve regret linear or sublinear in an effective dimension that is small on real graphs, allowing preference learning from tens of evaluations on thousands of items.
From Bandits to Experts: A Tale of Domination and In- dependence
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Spectral bandits for smooth graph functions
Spectral bandit algorithms for graph-smooth payoffs achieve regret linear or sublinear in an effective dimension that is small on real graphs, allowing preference learning from tens of evaluations on thousands of items.