67 citations · 93 across the 19 of their papers we have counts for
6 papers · 1 filter
Early-stopped aggregation: Adaptive inference with computational efficiency
Ilsang Ohn, Shitao Fan, Jungbin Jun +1
When considering a model selection or, more generally, an aggregation approach for adaptive statistical inference, it is often necessary to compute estimators over a wide range of…
Variational bagging: a robust approach for Bayesian uncertainty quantification
Shitao Fan, Ilsang Ohn, David Dunson +1
Variational Bayes methods are popular due to their computational efficiency and adaptability to diverse applications. In specifying the variational family, mean-field classes are c…
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…
Adaptive variational Bayes: Optimality, computation and applications
Ilsang Ohn, Lizhen Lin
In this paper, we explore adaptive inference based on variational Bayes. Although several studies have been conducted to analyze the contraction properties of variational posterior…
Optimal Bayesian estimation of Gaussian mixtures with growing number of components
Ilsang Ohn, Lizhen Lin
We study Bayesian estimation of finite mixture models in a general setup where the number of components is unknown and allowed to grow with the sample size. An assumption on growin…
Nonconvex sparse regularization for deep neural networks and its optimality
Ilsang Ohn, Yongdai Kim
Recent theoretical studies proved that deep neural network (DNN) estimators obtained by minimizing empirical risk with a certain sparsity constraint can attain optimal convergence…