most citedA Mean Field Approach to Empirical Bayes Estimation in High-dimensional Linear Regression

2 citations · 2 across the 1 of their papers we have counts for

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6 papers

math.ST20262 cited

A Mean Field Approach to Empirical Bayes Estimation in High-dimensional Linear Regression

Sumit Mukherjee, Bodhisattva Sen, Subhabrata Sen

We study empirical Bayes estimation in high-dimensional linear regression. To facilitate computationally efficient estimation of the underlying prior, we adopt a variational empiri…

math.ST2025

Wasserstein-Cramér-Rao Theory of Unbiased Estimation

Nicolás García Trillos, Adam Quinn Jaffe, Bodhisattva Sen

The quantity of interest in the classical Cramér-Rao theory of unbiased estimation (e.g., the Cramér-Rao lower bound, its exact attainment for exponential families, and asymptoti…

math.ST2025

Estimation of Algebraic Sets: Extending PCA Beyond Linearity

Alberto González-Sanz, Gilles Mordant, Álvaro Samperio +1

An algebraic set is defined as the zero locus of a system of real polynomial equations. In this paper we address the problem of recovering an unknown algebraic set fr…

math.ST2025

Variational Inference for Latent Variable Models in High Dimensions

Chenyang Zhong, Sumit Mukherjee, Bodhisattva Sen

Variational inference (VI) is a popular method for approximating intractable posterior distributions in Bayesian inference and probabilistic machine learning. In this paper, we int…

stat.ME2025

Constrained Denoising, Empirical Bayes, and Optimal Transport

Adam Quinn Jaffe, Nikolaos Ignatiadis, Bodhisattva Sen

In the statistical problem of denoising, Bayes and empirical Bayes methods can "overshrink" their output relative to the latent variables of interest. This work is focused on const…

stat.ME2025

Multivariate Distribution-Free Nonparametric Testing: Generalizing Wilcoxon's Tests via Optimal Transport

Zhen Huang, Bodhisattva Sen

This paper reviews recent advancements in the application of optimal transport (OT) to multivariate distribution-free nonparametric testing. Inspired by classical rank-based method…