7 papers
Adaptive Bayesian Online Learning via Expert Aggregation
Jungbin Jun, Ilsang Ohn
Bayesian online learning promises uncertainty-aware prediction on data streams, but its performance hinges on inferential choices, including learning rates, prior distributions and…
Online conformal inference with retrospective adjustment for faster adaptation to distribution shift
Jungbin Jun, Ilsang Ohn
Conformal prediction has emerged as a powerful framework for constructing distribution-free prediction sets with guaranteed coverage assuming only the exchangeability assumption. H…
Rate-optimal neural boundary detection from unlabeled noisy images
Kyeongho Kim, Ilsang Ohn
We study boundary detection for unlabeled noisy images from a statistical perspective. The aim is to recover an unknown object region from raw intensity observations without pixel-…
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…
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…
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…