Noisy predictions only marginally better than random guessing suffice to provably reduce the search space in exact exponential algorithms for subset selection problems, with runtime speedup scaling smoothly with prediction quality under pairwise independence or no accuracy knowledge.
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Best-action queries yield Õ(min{T/k, √(T-k)}) regret for i.i.d. stochastic rewards but only Ω(√(T-k)) regret for correlated stochastic or adversarial rewards in the bandit-feedback model.
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Learning Augmented Exact Exponential Algorithms
Noisy predictions only marginally better than random guessing suffice to provably reduce the search space in exact exponential algorithms for subset selection problems, with runtime speedup scaling smoothly with prediction quality under pairwise independence or no accuracy knowledge.
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Multi-Armed Bandits With Best-Action Queries
Best-action queries yield Õ(min{T/k, √(T-k)}) regret for i.i.d. stochastic rewards but only Ω(√(T-k)) regret for correlated stochastic or adversarial rewards in the bandit-feedback model.