67 citations · 93 across the 19 of their papers we have counts for
4 papers · 1 filter
Fast rates in Bayesian online learning with approximate posteriors
Ilsang Ohn
Exact Bayes prediction enjoys fast predictive regret guarantees, but exact posterior updating or representation may be too costly for online use. We study when these statistical gu…
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…
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…