12 papers
Normal approximations in nonparametric empirical Bayes
Jiafeng Chen, Nabarun Deb, Nikolaos Ignatiadis
Empirical Bayes analyses routinely model noisy measurements of latent parameters as normal, justifying this by an informal appeal to the central limit theorem (CLT). This paper put…
Fluctuations in random field Ising models
Seunghyun Lee, Nabarun Deb, Sumit Mukherjee
This paper establishes a CLT for linear statistics of the form with quantitative Berry-Esseen bounds, where is an observa…
No-Regret Generative Modeling via Parabolic Monge-Ampère PDE
Nabarun Deb, Tengyuan Liang
We introduce a novel generative modeling framework based on a discretized parabolic Monge-Ampère PDE, which emerges as a continuous limit of the Sinkhorn algorithm commonly used i…
Gibbs Measures with Multilinear Forms
Sohom Bhattacharya, Nabarun Deb, Sumit Mukherjee
In this paper, we study a class of multilinear Gibbs measures with Hamiltonian given by a generalized -statistic and with a general base measure. Expressing the asympto…
LDP for Inhomogeneous U-Statistics
Sohom Bhattacharya, Nabarun Deb, Sumit Mukherjee
In this paper we derive a Large Deviation Principle (LDP) for inhomogeneous U/V-statistics of a general order. Using this, we derive a LDP for two types of statistics: random multi…
Parametric Mean-Field empirical Bayes in high-dimensional linear regression
Seunghyun Lee, Nabarun Deb
In this paper, we consider the problem of parametric empirical Bayes estimation of an i.i.d. prior in high-dimensional Bayesian linear regression, with random design. We obtain the…