| 2024 | ICLR | $t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence. | Juno Kim, Jaehyuk Kwon, Mincheol Cho, Hyunjong Lee, Joong-Ho Won |
| 2024 | ICML | StrWAEs to Invariant Representations. | Hyunjong Lee, Yedarm Seong, Sungdong Lee, Joong-Ho Won |
| 2022 | ICML | Statistical inference with implicit SGD: proximal Robbins-Monro vs. Polyak-Ruppert. | Yoonhyung Lee, Sungdong Lee, Joong-Ho Won |
| 2021 | ICML | Learning from Nested Data with Ornstein Auto-Encoders. | Youngwon Choi, Sungdong Lee, Joong-Ho Won |
| 2020 | ICML | Principled learning method for Wasserstein distributionally robust optimization with local perturbations. | Yongchan Kwon, Wonyoung Kim, Joong-Ho Won, Myunghee Cho Paik |
| 2019 | AISTATS | Optimal Minimization of the Sum of Three Convex Functions with a Linear Operator. | Seyoon Ko, Joong-Ho Won |
| 2019 | ICML | Projection onto Minkowski Sums with Application to Constrained Learning. | Joong-Ho Won, Jason Xu, Kenneth Lange |
| 2019 | IJCAI | Ornstein Auto-Encoders. | Youngwon Choi, Joong-Ho Won |
| 2018 | AISTATS | Nonparametric Sharpe Ratio Function Estimation in Heteroscedastic Regression Models via Convex Optimization. | Seung-Jean Kim, Johan Lim, Joong-Ho Won |
| 2016 | MICCAI | Ensemble of Deep Convolutional Neural Networks for Prognosis of Ischemic Stroke. | Youngwon Choi, Yongchan Kwon, Han-Byul Lee, Beomjoon Kim, Myunghee Cho Paik, Joong-Ho Won |