Preprints and in preparation

1. Ilsang Ohn. (2024). Deep Bayesian density regression. In preparation.

2. Jongjin Lee, Ilsang Ohn, Insung Kong, and Yongdai Kim (2024). Knowledge distillation of uncertainty using deep latent factor models. In preparation.

3. Ilsang Ohn and Sangmoon Han. (2024). Optimal high-dimensional nonparametric regression with variational neural networks. In preparation.

4. Shitao Fan, Ilsang Ohn, and Lizhen Lin. (2024). Robust Bayesian inference via variational bagging. In preparation.

5. Ilsang Ohn. (2024). Variational Bayes with hierarchical priors. In preparation.

6. Meng Qiu, Sally Paganin, Ilsang Ohn, and Lizhen Lin. (2024). Bayesian nonparametric latent class analysis with different item types. In preparation.

7. Ilsang Ohn. (2022). The convergent Indian buffet process. arXiv preprint arXiv:2206.08002. [arXiv] 

Peer-reviewed articles

1. Ilsang Ohn, Lizhen Lin, and Yongdai Kim. (2024+). A Bayesian sparse factor model with adaptive posterior concentration. Accepted at Bayesian Analysis. [Link][arXiv] 

2. Ilsang Ohn and Lizhen Lin. (2024). Adaptive variational Bayes: Optimality, computation and applications.  The Annals of Statistics, 52(1), 335-363. [Link][arXiv] 

3. 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] 

4. 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.

5. 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]

6. Ilsang Ohn and Lizhen Lin. (2023). Optimal Bayesian estimation of Gaussian mixtures with growing number of components. Bernoulli, 29(2), 1195-1218. [Link][arXiv]

7. 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] 

8. 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] 

9. Ilsang Ohn and Yongdai Kim. (2022). Posterior consistency of factor dimensionality in high-dimensional sparse factor models. Bayesian Analysis. 17(2), 491-514 [Link] 

10. Ilsang Ohn and Yongdai Kim. (2022). Nonconvex sparse regularization for deep neural networks and its optimality. Neural Computation, 34(2), 476-517. [Link][arXiv] 

11. 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]

12. Yongdai Kim, Ilsang Ohn, and Dongha Kim. (2021). Fast convergence rates of deep neural networks for classification. Neural Networks, 138, 179-197. [Link] 

13. 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]

14. Ilsang Ohn and Yongdai Kim. (2019). Smooth function approximation by deep neural networks with general activation functions. Entropy, 21(7), 627. [Link] 

15. 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] 

16. 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] 

Peer-reviewed articles (Domestic journals, in English)

1. Ilsang Ohn, Seung Beom Seo, Seonghyeon Kim, Young-Oh Kim, and Yongdai Kim. (2020). Uncertainty decomposition in climate-change impact assessments: a Bayesian perspective. Communications for Statistical Applications and Methods, 27(1), 109-128. [Link] 

2. Ilsang Ohn, Yongdai Kim, and Young-Oh Kim. (2018). Uncertainty decomposition in water resources projection considering interaction effects. Journal of Korea Water Resources Association. 51(spc), 1067-1078. [Link] 

3. Yongdai Kim, Woosung Kim, Ilsang Ohn, and Young-Oh Kim. (2017). Leave-one-out Bayesian model averaging for probabilistic ensemble forecasting. Communications for Statistical Applications and Methods, 24(1), 67-80. [Link] 

Thesis

Asymptotic analysis of machine learning algorithms. 2020. Seoul National University. [link]