The MLA-UCB algorithm uses ML-generated surrogate rewards from auxiliary data to provably lower cumulative regret in multi-armed bandits, achieving asymptotic optimality under joint Gaussian assumptions without requiring knowledge of the true-surrogate covariance.
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Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards
The MLA-UCB algorithm uses ML-generated surrogate rewards from auxiliary data to provably lower cumulative regret in multi-armed bandits, achieving asymptotic optimality under joint Gaussian assumptions without requiring knowledge of the true-surrogate covariance.