Introduces tangential Bayes denoiser for Riemannian Gaussian mixtures on manifolds via spectral Laplace-Beltrami approximation, with nearly Bayes risk in low noise and minimax optimality on the circle.
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2 Pith papers cite this work, alongside 137 external citations. Polarity classification is still indexing.
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Pith papers citing it
137
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years
2026 2verdicts
UNVERDICTED 2representative citing papers
Witness motifs in constrained geometric graphs saturate Weyl bounds on Laplacian perturbations under heavy-tailed noise, with new metrics SC and S3I to distinguish noise-driven spectral effects.
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Nonparametric Riemannian Empirical Bayes, and Denoising Measurements on Manifolds
Introduces tangential Bayes denoiser for Riemannian Gaussian mixtures on manifolds via spectral Laplace-Beltrami approximation, with nearly Bayes risk in low noise and minimax optimality on the circle.
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Spectral Effects Of Heavy-Tailed Vertex Noise In Geometric Graphs
Witness motifs in constrained geometric graphs saturate Weyl bounds on Laplacian perturbations under heavy-tailed noise, with new metrics SC and S3I to distinguish noise-driven spectral effects.