8 papers
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…
Nonparametric undirected graphical model selection using diffusion models
Hyeok Kyu Kwon, Myeonggu Kang, Minwoo Chae +1
Undirected graphical models provide a fundamental framework for representing conditional independence structures among high-dimensional random variables. While undirected graphical…
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…
A monotone single index model for spatially referenced multistate current status data
Snigdha Das, Minwoo Chae, Debdeep Pati +1
Assessment of multistate disease progression is commonplace in biomedical research, such as, in periodontal disease (PD). However, the presence of multistate current status endpoin…
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…