GraphDR-LinUCB projects contextual bandit arms onto a graph's low-frequency eigenspace to obtain the first Õ(k√T) regret bound under approximate smoothness, with a spectral predictor Γ_k that matches outcomes on five of six real datasets.
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Graph Dimensionality Reduction for Contextual Bandits: Structure-Specific Regret Bounds under Approximate Smoothness and Noisy Eigenspaces
GraphDR-LinUCB projects contextual bandit arms onto a graph's low-frequency eigenspace to obtain the first Õ(k√T) regret bound under approximate smoothness, with a spectral predictor Γ_k that matches outcomes on five of six real datasets.