4 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…
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…
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…
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…