INN: A Method Identifying Clean-annotated Samples via Consistency Effect in Deep Neural Networks. - Dongha Kim; Yongchan Choi; Kunwoong Kim; Yongdai Kim (CoRR, 2021)

Learning fair representation with a parametric integral probability metric. - Dongha Kim; Kunwoong Kim; Insung Kong; Ilsang Ohn; Yongdai Kim (ICML, 2022)

SLIDE: A surrogate fairness constraint to ensure fairness consistency. - Kunwoong Kim; Ilsang Ohn; Sara Kim; Yongdai Kim (Neural Networks, 2022)

ODIM: an efficient method to detect outliers via inlier-memorization effect of deep generative models. - Dongha Kim; Jaesung Hwang; Jongjin Lee; Kunwoong Kim; Yongdai Kim (CoRR, 2023)

Improving Performance of Semi-Supervised Learning by Adversarial Attacks. - Dongyoon Yang; Kunwoong Kim; Yongdai Kim (CoRR, 2023)

Lq regularization for fair artificial intelligence robust to covariate shift. - Seonghyeon Kim; Sara Kim; Kunwoong Kim; Yongdai Kim (Stat. Anal. Data Min., 2023)

IOFM: Using the Interpolation Technique on the Over-Fitted Models to Identify Clean-Annotated Samples. - Dongha Kim; Yongchan Choi; Kunwoong Kim; Ilsang Ohn; Yongdai Kim (AAAI, 2024)

ODIM: Outlier Detection via Likelihood of Under-Fitted Generative Models. - Dongha Kim; Jaesung Hwang; Jongjin Lee; Kunwoong Kim; Yongdai Kim (ICML, 2024)

Fairness Through Matching. - Kunwoong Kim; Insung Kong; Jongjin Lee; Minwoo Chae; Sangchul Park; Yongdai Kim (Trans. Mach. Learn. Res., 2025)

ReLU integral probability metric and its applications. - Yuha Park; Kunwoong Kim; Insung Kong; Yongdai Kim (CoRR, 2025)

Fair Clustering via Alignment. - Kunwoong Kim; Jihu Lee; Sangchul Park; Yongdai Kim (CoRR, 2025)

Fair Representation Learning for Continuous Sensitive Attributes Using Expectation of Integral Probability Metrics. - Insung Kong; Kunwoong Kim; Yongdai Kim (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

Fair Bayesian Model-Based Clustering. - Jihu Lee; Kunwoong Kim; Yongdai Kim (CoRR, 2025)