13 citations · 57 across the 18 of their papers we have counts for
12 papers · 1 filter
Bayesian online learning in the one-pass regime: Frequentist validity and uncertainty quantification
Jeyong Lee, Junhyeok Choi, Dongguen Kim +1
Bayesian online learning provides a coherent framework for sequential inference. However, its theoretical understanding remains limited, particularly in the one-pass setting. Exist…
Online Bernstein-von Mises theorem
Jeyong Lee, Junhyeok Choi, Minwoo Chae
Online learning is an inferential paradigm in which parameters are updated incrementally from sequentially available data, in contrast to batch learning, where the entire dataset i…
Nonparametric estimation of a factorizable density using diffusion models
Hyeok Kyu Kwon, Dongha Kim, Ilsang Ohn +1
In recent years, diffusion models, and more generally score-based deep generative models, have achieved remarkable success in various applications, including image and audio genera…
Advances in Bayesian model selection consistency for high-dimensional generalized linear models
Jeyong Lee, Minwoo Chae, Ryan Martin
Uncovering genuine relationships between a response variable of interest and a large collection of covariates is a fundamental and practically important problem. In the context of…
Minimax optimal density estimation using a shallow generative model with a one-dimensional latent variable
Hyeok Kyu Kwon, Minwoo Chae
A deep generative model yields an implicit estimator for the unknown distribution or density function of the observation. This paper investigates some statistical properties of the…
Rates of convergence for nonparametric estimation of singular distributions using generative adversarial networks
Jeyong Lee, Hyeok Kyu Kwon, Minwoo Chae
It is common in nonparametric estimation problems to impose a certain low-dimensional structure on the unknown parameter to avoid the curse of dimensionality. This paper considers…
Bayesian High-dimensional Semi-parametric Inference beyond sub-Gaussian Errors
Kyoungjae Lee, Minwoo Chae, Lizhen Lin
We consider a sparse linear regression model with unknown symmetric error under the high-dimensional setting. The true error distribution is assumed to belong to the locally -Hö…
Posterior asymptotics in Wasserstein metrics on the real line
Minwoo Chae, Pierpaolo De Blasi, Stephen G. Walker
In this paper, we use the class of Wasserstein metrics to study asymptotic properties of posterior distributions. Our first goal is to provide sufficient conditions for posterior c…
Applications of an algorithm for solving Fredholm equations of the first kind
Minwoo Chae, Ryan Martin, Stephen G. Walker
In this paper we use an iterative algorithm for solving Fredholm equations of the first kind. The basic algorithm is known and is based on an EM algorithm when involved functions a…
Bayesian Sparse Linear Regression with Unknown Symmetric Error
Minwoo Chae, Lizhen Lin, David B. Dunson
We study full Bayesian procedures for sparse linear regression when errors have a symmetric but otherwise unknown distribution. The unknown error distribution is endowed with a sym…
The semi-parametric Bernstein-von Mises theorem for regression models with symmetric errors
Minwoo Chae, Yongdai Kim, Bas Kleijn
In a smooth semi-parametric model, the marginal posterior distribution for a finite dimensional parameter of interest is expected to be asymptotically equivalent to the sampling di…
The semiparametric Bernstein-von Mises theorem for models with symmetric error
Minwoo Chae
In a smooth semiparametric model, the marginal posterior distribution of the finite dimensional parameter of interest is expected to be asymptotically equivalent to the sampling di…