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New algorithm adapts Lipschitz narrowing without knowing zooming dimension

By PulseAugur Editorial · [1 sources] ·

A new research paper introduces Dimension-Adaptive Batched Lipschitz Narrowing (A-BLiN), a method that removes the dependence on knowing the zooming dimension ($d_z$). The Count-Adaptive BLiN algorithm achieves a regret of $\widetilde{\mathcal O}_d(T^{(d_z+1)/(d_z+2)})$ with $\mathcal O_d(\log\log T)$ batches, even when $d_z$ is unknown. This advancement maintains optimal batch complexity in scenarios where the zooming dimension is not specified. AI

IMPACT This research may lead to more efficient optimization algorithms in machine learning by removing a dependency on knowing specific dimensional parameters.

RANK_REASON The cluster contains a single academic paper on a machine learning algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New algorithm adapts Lipschitz narrowing without knowing zooming dimension

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The cluster contains a single academic paper on a machine learning algorithm. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yasong Feng ·

    Dimension-Adaptive Batched Lipschitz Narrowing Without Knowing the Zooming Dimension

    arXiv:2609.05214v1 Announce Type: new 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 eliminat…

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