AI RESEARCH
A Reduction Algorithm for Markovian Contextual Linear Bandits
arXiv CS.LG
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ArXi:2603.12530v1 Announce Type: new Recent work shows that when contexts are drawn i.i.d., linear contextual bandits can be reduced to single-context linear bandits. This ``contexts are cheap" perspective is highly advantageous, as it allows for sharper finite-time analyses and leverages mature techniques from the linear bandit literature, such as those for misspecification and adversarial corruption.