Homogeneity detection for the high-dimensional generalized linear model. - Jong-June Jeon; Sunghoon Kwon; Hosik Choi (Comput. Stat. Data Anal., 2017)

The sparse Luce model. - Jong-June Jeon; Hosik Choi (Appl. Intell., 2018)

Primal path algorithm for compositional data analysis. - Jong-June Jeon; Yongdai Kim; Sungho Won; Hosik Choi (Comput. Stat. Data Anal., 2020)

EXoN: EXplainable encoder Network. - Seunghwan An; Hosik Choi; Jong-June Jeon (CoRR, 2021)

Learning Multiple Quantiles With Neural Networks. - Sang Jun Moon; Jong-June Jeon; Jason Sang Hun Lee; Yongdai Kim (J. Comput. Graph. Stat., 2021)

Learning a High-dimensional Linear Structural Equation Model via l1-Regularized Regression. - Gunwoong Park; Sang Jun Moon; Sion Park; Jong-June Jeon (J. Mach. Learn. Res., 2021)

Causally Disentangled Generative Variational AutoEncoder. - Seunghwan An; Kyungwoo Song; Jong-June Jeon (ECAI, 2023)

Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation. - Seunghwan An; Jong-June Jeon (NeurIPS, 2023)

Geodesic Multi-Modal Mixup for Robust Fine-Tuning. - Changdae Oh; Junhyuk So; Hoyoon Byun; YongTaek Lim; Minchul Shin; Jong-June Jeon; Kyungwoo Song (NeurIPS, 2023)

Distributional Variational AutoEncoder To Infinite Quantiles and Beyond Gaussianity. - Seunghwan An; Jong-June Jeon (CoRR, 2023)

Interpretable Water Level Forecaster with Spatiotemporal Causal Attention Mechanisms. - Sungchul Hong; Yunjin Choi; Jong-June Jeon (CoRR, 2023)

Uniform Pessimistic Risk and Optimal Portfolio. - Sungchul Hong; Jong-June Jeon (CoRR, 2023)

Joint Distributional Learning via Cramer-Wold Distance. - Seunghwan An; Jong-June Jeon (CoRR, 2023)

Balanced Marginal and Joint Distributional Learning via Mixture Cramer-Wold Distance. - Seunghwan An; Sungchul Hong; Jong-June Jeon (CoRR, 2023)

Quantile estimation for encrypted data. - Minje Park; Jaeseon Kim; Sungchul Shin; Cheolwoo Park; Jong-June Jeon; Soon-Sun Kwon; Hosik Choi (Appl. Intell., 2023)

Cryptocurrency Price Forecasting using Variational Autoencoder with Versatile Quantile Modeling. - Sungchul Hong; Seunghwan An; Jong-June Jeon (CIKM, 2024)

Unicorn: U-Net for Sea Ice Forecasting with Convolutional Neural Ordinary Differential Equations. - Jaesung Park; Sungchul Hong; Yoonseo Cho; Jong-June Jeon (CoRR, 2024)

Customization of latent space in semi-supervised Variational AutoEncoder. - Seunghwan An; Jong-June Jeon (Pattern Recognit. Lett., 2024)

Masked Language Modeling Becomes Conditional Density Estimation for Tabular Data Synthesis. - Seunghwan An; Gyeongdong Woo; Jaesung Lim 002; ChangHyun Kim; Sungchul Hong; Jong-June Jeon (AAAI, 2025)

Adaptive adversarial augmentation for molecular property prediction. - Soyoung Cho; Sungchul Hong; Jong-June Jeon (Expert Syst. Appl., 2025)

Does a Large Language Model Really Speak in Human-Like Language? - Mose Park; Yunjin Choi; Jong-June Jeon (CoRR, 2025)

Improving SMOTE via fusing conditional VAE for data-adaptive noise filtering. - Sungchul Hong; Seunghwan An; Jong-June Jeon (Appl. Intell., 2025)