| 2026 | AAAI | On the Information Processing of One-Dimensional Wasserstein Distances with Finite Samples. | Cheongjae Jang, Jong-Hyun Won, Soyeon Jun, Chun Kee Chung, Keehyoung Joo, Yung-Kyun Noh |
| 2024 | ICLR | Kernel Metric Learning for In-Sample Off-Policy Evaluation of Deterministic RL Policies. | Haanvid Lee, Tri Wahyu Guntara, Jongmin Lee, Yung-Kyun Noh, Kee-Eung Kim |
| 2023 | ICLR | Geometrically regularized autoencoders for non-Euclidean data. | Cheongjae Jang, Yonghyeon Lee, Yung-Kyun Noh, Frank C. Park |
| 2021 | ICML | Autoencoding Under Normalization Constraints. | Sangwoong Yoon, Yung-Kyun Noh, Frank Chongwoo Park |
| 2018 | ICML | K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning. | Jihun Hamm, Yung-Kyun Noh |
| 2017 | ICRA | Motion planning with movement primitives for cooperative aerial transportation in obstacle environment. | Hyoin Kim, Hyeonbeom Lee, Seungwon Choi, Yung-Kyun Noh, H. Jin Kim |
| 2017 | IJCNN | Transfer learning for automated optical inspection. | Seunghyeon Kim, Wooyoung Kim, Yung-Kyun Noh, Frank Chongwoo Park |
| 2015 | AAAI | Reward Shaping for Model-Based Bayesian Reinforcement Learning. | Hyeoneun Kim, Woosang Lim, Kanghoon Lee, Yung-Kyun Noh, Kee-Eung Kim |
| 2015 | AISTATS | Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation. | Hiroaki Sasaki, Yung-Kyun Noh, Masashi Sugiyama |
| 2014 | AISTATS | Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence. | Yung-Kyun Noh, Masashi Sugiyama, Song Liu, Marthinus Christoffel du Plessis, Frank Chongwoo Park, Daniel D. Lee |
| 2013 | CogSci | k-Nearest Neighbor Classification Algorithm for Multiple Choice Sequential Sampling. | Yung-Kyun Noh, Frank Chongwoo Park, Daniel D. Lee |
| 2008 | ICPR | Regularized discriminant analysis for transformation-invariant object recognition. | Yung-Kyun Noh, Jihun Ham, Daniel D. Lee |