Deep generative models

Outlier detection

Synthetic data generation & Data privacy

Semi-supervised learning

D.Kim, J.Hwang, J.Lee, K.Kim and Y.Kim. (2024) "ODIM: an efficient method to detect outliers via inlier-memorization effect of deep generative models". The 41st International Conference on Machine Learning (ICML 2024).  

D.Kim, Y.Choi, K.Kim and Y.Kim. (2024) "IOFM: Using the Interpolation Technique on Over-fitted Models to Identify Clean-annotated Samples". The 38th AAAI Conference on Artificial Intelligence (AAAI 2024). 

M.Chae, D.Kim, Y.Kim, and L.Lin. (2023) "A likelihood approach to nonparametric estimation of a singular distribution using deep generative models". Journal of Machine Learning Research. Vol. 24(77). pp. 1--42. 

D.Kim, K.Kim, I.Kong, I.Ohn, and Y.Kim. (2022) "Learning fair representation with a parametric integral probability metric". The 39th International Conference on Machine Learning (ICML 2022). 

Y.Kim, I.Ohn and D.Kim. (2021) "Fast convergence rates of deep neural networks for classification". Neural Networks. Vol. 138. pp.179-197. 

M.Kim, Y.Kim, D.Kim, Y.Kim and MC.Paik. (2021)"Kernel-convoluted Deep Neural Networks with Data Augmentation". The 35th AAAI Conference on Artificial Intelligence (AAAI 2021). 

D.Kim, J.Hwang and Y.Kim. (2020) "On casting importance weighted autoencoder to an EM algorithm to learn deep generative models". The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020).