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.
Sharp regret bounds for empirical Bayes and compound decision problems
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4representative citing papers
A Gamma-smoothed NPMLE for Poisson empirical Bayes achieves optimal nearly parametric rates for posterior means and enables asymptotically exact, shorter marginal coverage confidence sets under compact support.
Dirichlet-process Bayes and Newton quasi-Bayes g-modeling procedures for the Poisson compound decision problem merge in Hellinger distance and regret at rates controlled by the learning-rate exponent, with a multidimensional extension.
citing papers explorer
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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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Poisson Empirical Bayes via Gamma-Smoothed Nonparametric Maximum Likelihood
A Gamma-smoothed NPMLE for Poisson empirical Bayes achieves optimal nearly parametric rates for posterior means and enables asymptotically exact, shorter marginal coverage confidence sets under compact support.
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Merging of Bayes and quasi-Bayes empirical Bayes procedures for Poisson compound decisions
Dirichlet-process Bayes and Newton quasi-Bayes g-modeling procedures for the Poisson compound decision problem merge in Hellinger distance and regret at rates controlled by the learning-rate exponent, with a multidimensional extension.
- Sharp regret-Hellinger bounds for Gaussian empirical Bayes via polynomial approximation