Constrained bandit algorithms whose regret depends on the realized losses, split into a safety term and a bandit-learning term, with a matching lower bound.
With probability at least 1 − δ, SOLB guarantees that g⊤ i xt ≤ αi holds for every action a ∈ [K], constraint i ∈ [m], and round t ∈ [T ]
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Data-Dependent Regret Bounds for Constrained MABs
Constrained bandit algorithms whose regret depends on the realized losses, split into a safety term and a bandit-learning term, with a matching lower bound.