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Proposes algorithms for contextual slate bandits with generalized linear rewards under limited adaptivity, achieving regret bounds independent of the non-linearity parameter. The batched and rarely-switching algorithms are computationally efficient and empirically outperform baselines, including in a language model example selection task.
This paper introduces a hybrid Track-and-Stop algorithm for best arm identification in generalized linear bandits that unifies absolute and relative feedback. The authors propose a likelihood-ratio-based confidence sequence to adaptively allocate queries, demonstrating improved sample efficiency over baseline methods.