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.
Improved Algorithms for Linear Stochastic Bandits
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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.