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Found 3 papers, 1 papers with code
Optimal Regret with Limited Adaptivity for Generalized Linear Contextual Bandits
For our batch learning algorithm B-GLinCB, with $\Omega\left( \log{\log T} \right)$ batches, the regret scales as $\tilde{O}(\sqrt{T})$.
Learning Good Interventions in Causal Graphs via Covering
We study the causal bandit problem that entails identifying a near-optimal intervention from a specified set $A$ of (possibly non-atomic) interventions over a given causal graph.
Fairness and Welfare Quantification for Regret in Multi-Armed Bandits
Since NSW is known to satisfy fairness axioms, our approach complements the utilitarian considerations of average (cumulative) regret, wherein the algorithm is evaluated via the arithmetic mean of its expected rewards.
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