New hybrid least squares method tackles noisy data for function approximation

By PulseAugur Editorial · [1 sources] · 2026-05-26 04:00

Researchers have developed a new hybrid least squares method designed to improve function approximation from data containing significant noise. This approach combines Christoffel sampling with optimal experimental design to enhance computational efficiency and reduce sample complexity, particularly in high-noise scenarios. The method has been extended to handle convexity-constrained settings and has shown promising theoretical and numerical results, including applications in computational finance. AI

RANK_REASON The cluster contains a research paper detailing a new algorithmic approach. [lever_c_demoted from research: ic=1 ai=0.7]

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Yiming Xu

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arXiv cs.LG TIER_1 English(EN) · Ben Adcock, Bernhard Hientzsch, Akil Narayan, Yiming Xu · 2026-05-26 04:00

Hybrid least squares for learning functions from highly noisy data

arXiv:2507.02215v2 Announce Type: replace-cross Abstract: Motivated by the need for efficient estimation of conditional expectations, we consider a least-squares function approximation problem with heavily polluted data. Existing methods that are effective in the small-noise regi…

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Hybrid least squares for learning functions from highly noisy data

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