67 citations · 93 across the 19 of their papers we have counts for
20 papers
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…
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…
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…
Knowledge Distillation of Uncertainty using Deep Latent Factor Model
Sehyun Park, Jongjin Lee, Yunseop Shin +2
Deep ensembles deliver state-of-the-art, reliable uncertainty quantification, but their heavy computational and memory requirements hinder their practical deployments to real appli…
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 Bayesian sparse factor model with adaptive posterior concentration
Ilsang Ohn, Lizhen Lin, Yongdai Kim
In this paper, we propose a new Bayesian inference method for a high-dimensional sparse factor model that allows both the factor dimensionality and the sparse structure of the load…
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference
Insung Kong, Dongyoon Yang, Jongjin Lee +3
Bayesian approaches for learning deep neural networks (BNN) have been received much attention and successfully applied to various applications. Particularly, BNNs have the merit of…
Intrinsic and extrinsic deep learning on manifolds
Yihao Fang, Ilsang Ohn, Vijay Gupta +1
We propose extrinsic and intrinsic deep neural network architectures as general frameworks for deep learning on manifolds. Specifically, extrinsic deep neural networks (eDNNs) pres…
The convergent Indian buffet process
Ilsang Ohn
We propose a new Bayesian nonparametric prior for latent feature models, which we call the convergent Indian buffet process (CIBP). We show that under the CIBP, the number of laten…
Masked Bayesian Neural Networks : Computation and Optimality
Insung Kong, Dongyoon Yang, Jongjin Lee +2
As data size and computing power increase, the architectures of deep neural networks (DNNs) have been getting more complex and huge, and thus there is a growing need to simplify su…
SLIDE: a surrogate fairness constraint to ensure fairness consistency
Kunwoong Kim, Ilsang Ohn, Sara Kim +1
As they have a vital effect on social decision makings, AI algorithms should be not only accurate and but also fair. Among various algorithms for fairness AI, learning a prediction…
Learning fair representation with a parametric integral probability metric
Dongha Kim, Kunwoong Kim, Insung Kong +2
As they have a vital effect on social decision-making, AI algorithms should be not only accurate but also fair. Among various algorithms for fairness AI, learning fair representati…
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…
Smooth function approximation by deep neural networks with general activation functions
Ilsang Ohn, Yongdai Kim
There has been a growing interest in expressivity of deep neural networks. However, most of the existing work about this topic focuses only on the specific activation function such…
Fast convergence rates of deep neural networks for classification
Yongdai Kim, Ilsang Ohn, Dongha Kim
We derive the fast convergence rates of a deep neural network (DNN) classifier with the rectified linear unit (ReLU) activation function learned using the hinge loss. We consider t…