68 citations · 353 across the 57 of their papers we have counts for
7 papers · 1 filter
On Statistical Optimality of Variational Bayes
Debdeep Pati, Anirban Bhattacharya, Yun Yang
The article addresses a long-standing open problem on the justification of using variational Bayes methods for parameter estimation. We provide general conditions for obtaining opt…
-Variational Inference with Statistical Guarantees
Yun Yang, Debdeep Pati, Anirban Bhattacharya
We propose a family of variational approximations to Bayesian posterior distributions, called -VB, with provable statistical guarantees. The standard variational approximation i…
Frequentist coverage and sup-norm convergence rate in Gaussian process regression
Yun Yang, Anirban Bhattacharya, Debdeep Pati
Gaussian process (GP) regression is a powerful interpolation technique due to its flexibility in capturing non-linearity. In this paper, we provide a general framework for understa…
Bayes Shrinkage at GWAS scale: Convergence and Approximation Theory of a Scalable MCMC Algorithm for the Horseshoe Prior
James E. Johndrow, Paulo Orenstein, Anirban Bhattacharya
The horseshoe prior is frequently employed in Bayesian analysis of high-dimensional models, and has been shown to achieve minimax optimal risk properties when the truth is sparse.…
Compressed Covariance Estimation With Automated Dimension Learning
Gautam Sabnis, Debdeep Pati, Anirban Bhattacharya
We propose a method for estimating a covariance matrix that can be represented as a sum of a low-rank matrix and a diagonal matrix. The proposed method compresses high-dimensional…
Empirical Bayes, SURE and Sparse Normal Mean Models
Xianyang Zhang, Anirban Bhattacharya
This paper studies the sparse normal mean models under the empirical Bayes framework. We focus on the mixture priors with an atom at zero and a density component centered at a data…
Adaptive posterior convergence rates in non-linear latent variable models
Shuang Zhou, Debdeep Pati, Anirban Bhattacharya +1
Non-linear latent variable models have become increasingly popular in a variety of applications. However, there has been little study on theoretical properties of these models. In…