Web: http://arxiv.org/abs/2110.09722

Jan. 24, 2022, 2:11 a.m. | Yasong Feng, Zengfeng Huang, Tianyu Wang

cs.LG updates on arXiv.org arxiv.org

In this paper, we study Lipschitz bandit problems with batched feedback,
where the expected reward is Lipschitz and the reward observations are
communicated to the player in batches. We introduce a novel landscape-aware
algorithm, called Batched Lipschitz Narrowing (BLiN), that optimally solves
this problem. Specifically, we show that for a $T$-step problem with Lipschitz
reward of zooming dimension $d_z$, our algorithm achieves theoretically optimal
regret rate of $ \widetilde{\mathcal{O}} \left( T^{\frac{d_z + 1}{d_z + 2}}
\right) $ using only $ …


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