Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function

Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/optimal-uncertainty-bounds-for-multivariate-kernel-regression-under-bounded-noise-a-gaussian-process-based-dual-function

stale

Proof freshness: stale
Proof status: unverified
Display score: 2/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID optimal-uncertainty-bounds-for-multivariate-kernel-regression-under-bounded-noise-a-gaussian-process-based-dual-function | Route /signal-canvas/optimal-uncertainty-bounds-for-multivariate-kernel-regression-under-bounded-noise-a-gaussian-process-based-dual-function

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Evidence Receipt

Route status: building

Claims: 0

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function

PDF: https://arxiv.org/pdf/2603.16481v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-03-19T18:48:05.835Z

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Subject: Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function

Verdict

Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function

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