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New kernel method adapts to learned multivariable structure for function approximation

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced total sensitivity kernels (TSKs), a novel method designed to improve the approximation of complex multivariable black-box functions using limited data. TSKs leverage weighted ANOVA kernels that adapt to the underlying multivariable structure of the function. The method learns factors that characterize input importance across various interactions and main effects, offering a kernel-dependent measure of input sensitivity related to Sobol indices. Experiments show that this adaptive kernel approach significantly enhances approximation accuracy compared to standard product kernels. AI

RANK_REASON The cluster contains a research paper detailing a new kernel method for approximation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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New kernel method adapts to learned multivariable structure for function approximation

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The cluster contains a research paper detailing a new kernel method for approximation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · John E. Darges, Laura Weidensager ·

    A Weighted Kernel Method for Approximation that Adapts to Learned Multivariable Structure

    arXiv:2609.16606v1 Announce Type: new Abstract: Approximating the input-output behavior of a multivariable black-box function from limited data is challenging when blind to the importance of its inputs and their interactions. We introduce total sensitivity kernels (TSKs), a metho…

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