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New Recursive TensorSketch method reduces random bits for matrix trace estimation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for estimating the trace of large implicit matrices, which can be computationally intensive. This novel approach, termed Recursive TensorSketch, significantly reduces the number of random bits required compared to existing methods like the Hutchinson trace estimator. The proposed technique achieves this efficiency while maintaining an unbiased estimate and bounding the variance polynomially with respect to the matrix dimensions, addressing limitations of prior work. AI

IMPACT This research could lead to more efficient computation for large-scale machine learning models that rely on matrix operations.

RANK_REASON The cluster contains a research paper detailing a new algorithmic method for a specific computational problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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New Recursive TensorSketch method reduces random bits for matrix trace estimation

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The cluster contains a research paper detailing a new algorithmic method for a specific computational problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mohammad Azhar Khan, Rameshwar Pratap, Amit Sharma ·

    Accurate Trace Estimation with Fewer Random Bits via Recursive TensorSketch

    arXiv:2609.18577v1 Announce Type: new Abstract: We consider the problem of estimating the trace of an implicit matrix $\mathbf{A} \in \mathbb{R}^{d^p\times d^p}$ that can only be accessed through matrix-vector products queries. The \textit{Hutchinson trace estimator}% ~\cite{Gira…

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