User dependent interface based on facial expression. - Euisun Kim; Yunseok Lee; Sungyoon Lee; Jaehyo Kim (GCCE, 2013)

Development and evaluation of myoelectric driving interface. - Jaesung Oh; Minsuk Kwon; Youngwon Kim; Jungsoo Kim; Sungyoon Lee; Jaehyo Kim (ICCE, 2013)

Myo-Signal Based Intuitive Driving Interface: Development and Evaluation. - Jinuk Kim; Sungyoon Lee; Jaehyo Kim (BIC-TA, 2014)

Lipschitz-Certifiable Training with a Tight Outer Bound. - Sungyoon Lee; Jaewook Lee 001; Saerom Park (NeurIPS, 2020)

Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples. - Sungyoon Lee; Woojin Lee; Jinseong Park 001; Jaewook Lee 001 (NeurIPS, 2021)

A Reparametrization-Invariant Sharpness Measure Based on Information Geometry. - Cheongjae Jang; Sungyoon Lee; Frank C. Park 001; Yung-Kyun Noh (NeurIPS, 2022)

Variational cycle-consistent imputation adversarial networks for general missing patterns. - Woojin Lee; Sungyoon Lee; Junyoung Byun; Hoki Kim; Jaewook Lee 001 (Pattern Recognit., 2022)

A new characterization of the edge of stability based on a sharpness measure aware of batch gradient distribution. - Sungyoon Lee; Cheongjae Jang (ICLR, 2023)

Implicit Jacobian regularization weighted with impurity of probability output. - Sungyoon Lee; Jinseong Park 001; Jaewook Lee 001 (ICML, 2023)

The Mean Squared Error of the Ridgeless Least Squares Estimator under General Assumptions on Regression Errors. - Sungyoon Lee; Sokbae Lee (CoRR, 2023)

GradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization. - Sungyoon Lee; Hoki Kim; Jaewook Lee 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Bridged adversarial training. - Hoki Kim; Woojin Lee; Sungyoon Lee; Jaewook Lee 001 (Neural Networks, 2023)

Sliced Wasserstein adversarial training for improving adversarial robustness. - Woojin Lee; Sungyoon Lee; Hoki Kim; Jaewook Lee (J. Ambient Intell. Humaniz. Comput., 2024)

Prediction Risk and Estimation Risk of the Ridgeless Least Squares Estimator under General Assumptions on Regression Errors. - Sungyoon Lee; Sokbae Lee (ICLR, 2025)