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New algorithms estimate gradients using only comparisons

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new algorithms for gradient testing and estimation using only a comparison oracle, which determines which of two points yields a higher function value. For smooth functions, an algorithm can test if a normalized gradient is close to a given vector with a constant number of queries. Additionally, an algorithm can estimate the normalized gradient using $O(n\log(1/\varepsilon))$ queries, which has been proven to be optimal. A quantum algorithm has also been developed that achieves this estimation with $O(\log (n/\varepsilon))$ queries. AI

IMPACT This research could lead to more efficient methods for training machine learning models by improving gradient estimation techniques.

RANK_REASON Academic paper detailing new algorithms for gradient testing and estimation. [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 algorithms estimate gradients using only comparisons

COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xiwen Tao, Chenyi Zhang, Helin Wang, Yexin Zhang, Tongyang Li ·

    Gradient Testing and Estimation by Comparisons

    arXiv:2405.11454v3 Announce Type: replace Abstract: We study gradient testing and gradient estimation of smooth functions using only a comparison oracle that, given two points, indicates which one has the larger function value. For any smooth $f\colon\mathbb R^n\to\mathbb R$, $\m…

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