Dimension-Adaptive Batched Lipschitz Narrowing Without Knowing the Zooming Dimension

Abstract:The Appropriately Combined Edge-length (ACE) sequence in A-BLiN depends on the zooming dimension $d_z$. This note removes that dependence. The next edge length is selected from the number of cubes that survive the preceding elimination. The resulting Count-Adaptive BLiN algorithm does not use $d_z$ or the zooming constant $C_z$, yet it attains $\widetilde{\mathcal O}_d(T^{(d_z+1)/(d_z+2)})$ regret with $\mathcal O_d(\log\log T)$ batches. Together with the adaptive-grid lower bound in Theorem 10 of the original paper, the optimal batch complexity remains $\Theta_d(\log\log T)$ when $d_z$ is unknown.

Machine Learning (cs.LG)

arXiv:2609.05214 [cs.LG]

(or arXiv:2609.05214v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.05214 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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