Meng Qiu, Sally Paganin, Ilsang Ohn, and Lizhen Lin. (2025+). Bayesian nonparametric latent class analysis with different item types. Accepted to Psychological Methods.
Ilsang Ohn and Jisu Park^. (2025). Fast full conformal prediction for multiple test points. AIMS Mathematics. 10(3), 5143-5157. [Link]
Ilsang Ohn, Lizhen Lin, and Yongdai Kim. (2024). A Bayesian sparse factor model with adaptive posterior concentration. Bayesian Analysis, 19(4), 1277-1301. [Link][arXiv]
Ilsang Ohn and Lizhen Lin. (2024). Adaptive variational Bayes: Optimality, computation and applications. The Annals of Statistics, 52(1), 335-363. [Link][arXiv]
Yihao Fang, Ilsang Ohn, Vijay Gupta, and Lizhen Lin. (2024). Intrinsic and extrinsic deep learning on manifolds. Electronic Journal of Statistics, 18(1), 1160-1184. [Link][arXiv]
Dongha Kim, Yongchan Choi, Kunwoong Kim, Ilsang Ohn, and Yongdai Kim. (2024). IOFM: Using the interpolation technique on the over-fitted models to identify clean-annotated samples. AAAI Conference on Artificial Intelligence. [Link]
Insung Kong, Dongyoon Yang, Jongjin Lee, Ilsang Ohn, Gyuseung Baek, and Yongdai Kim. (2023). Masked Bayesian neural networks: Theoretical guarantee and its posterior inference. International Conference on Machine Learning. [Link][arXiv]
Ilsang Ohn and Lizhen Lin. (2023). Optimal Bayesian estimation of Gaussian mixtures with growing number of components. Bernoulli, 29(2), 1195-1218. [Link][arXiv]
Kunwoong Kim, Ilsang Ohn, Sara Kim, and Yongdai Kim. (2022). SLIDE: A surrogate fairness constraint to ensure fairness-consistency. Neural Networks, 154, 441-454. [Link][arXiv]
Dongha Kim, Kunwoong Kim, Insung Kong, Ilsang Ohn, and Yongdai Kim. (2022). Learning fair representation with a parametric integral probability metric. International Conference on Machine Learning. [Link][arXiv]
Ilsang Ohn and Yongdai Kim. (2022). Posterior consistency of factor dimensionality in high-dimensional sparse factor models. Bayesian Analysis. 17(2), 491-514 [Link]
Ilsang Ohn and Yongdai Kim. (2022). Nonconvex sparse regularization for deep neural networks and its optimality. Neural Computation, 34(2), 476-517. [Link][arXiv]
Ilsang Ohn, Seonghyeon Kim, Seung Beom Seo, Young-Oh Kim, and Yongdai Kim. (2021). Model-wise uncertainty decomposition in multi-model ensemble hydrological projections. Stochastic Environmental Research and Risk Assessment, 35(12), 2549–2565. [Link]
Yongdai Kim, Ilsang Ohn, and Dongha Kim. (2021). Fast convergence rates of deep neural networks for classification. Neural Networks, 138, 179-197. [Link]
Ilsang Ohn, Seonghyeon Kim, Seung Beom Seo, Young-Oh Kim, and Yongdai Kim. (2020). Bayesian uncertainty decomposition for hydrological projections. Journal of the Korean Statistical Society, 49(3), 953-975. [Link]
Ilsang Ohn and Yongdai Kim. (2019). Smooth function approximation by deep neural networks with general activation functions. Entropy, 21(7), 627. [Link]
Yongdai Kim, Ilsang Ohn, Jae-Kyoung Lee, and Young-Oh Kim. (2019). Generalizing uncertainty decomposition theory in climate change impact assessments. Journal of Hydrology X, 3, 100024. [Link]
Yongdai Kim, Young-Oh Kim, Jaeseok Kim, Woosung Kim, and Ilsang Ohn. (2016). Scaled ridge estimator and its application to multimodel ensemble approaches for climate prediction. Journal of the Korean Statistical Society, 45(2), 307-313. [Link]