AI RESEARCH
One Good Source is All You Need: Near-Optimal Regret for Bandits under Heterogeneous Noise
arXiv CS.LG
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ArXi:2602.14474v2 Announce Type: replace We study $K$-armed Multiarmed Bandit (MAB) problem with $M$ heterogeneous data sources, each exhibiting unknown and distinct noise variances $\{\sigma_j^2\}_{j=1}^M$. The learner's objective is standard MAB regret minimization, with the additional complexity of adaptively selecting which data source to query from at each round.