Proposes KIPLMC algorithms based on joint parameter-latent diffusions with nonasymptotic Wasserstein-2 rates under strong concavity, claiming improved dimension dependence over prior Langevin methods.
Gradient flows for empirical bayes in high- dimensional linear models
2 Pith papers cite this work. Polarity classification is still indexing.
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Denoising regret of NPMLE and sieve methods in nonparametric empirical Bayes is bounded by the exact normality rate plus a marginal CLT approximation error term.
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Kinetic Interacting Particle Langevin Monte Carlo
Proposes KIPLMC algorithms based on joint parameter-latent diffusions with nonasymptotic Wasserstein-2 rates under strong concavity, claiming improved dimension dependence over prior Langevin methods.
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Normal approximations in nonparametric empirical Bayes
Denoising regret of NPMLE and sieve methods in nonparametric empirical Bayes is bounded by the exact normality rate plus a marginal CLT approximation error term.