Kim, Yongdai

19 publications

ICML 2025 Fair Clustering via Alignment Kunwoong Kim, Jihu Lee, Sangchul Park, Yongdai Kim

TMLR 2025 Fairness Through Matching Kunwoong Kim, Insung Kong, Jongjin Lee, Minwoo Chae, Sangchul Park, Yongdai Kim

NeurIPS 2025 Knowledge Distillation of Uncertainty Using Deep Latent Factor Model Sehyun Park, Jongjin Lee, Yunseop Shin, Ilsang Ohn, Yongdai Kim

JMLR 2025 Posterior Concentrations of Fully-Connected Bayesian Neural Networks with General Priors on the Weights Insung Kong, Yongdai Kim

CVPR 2025 TAROT: Towards Essentially Domain-Invariant Robustness with Theoretical Justification Dongyoon Yang, Jihu Lee, Yongdai Kim

ICML 2025 Tensor Product Neural Networks for Functional ANOVA Model Seokhun Park, Insung Kong, Yongchan Choi, Chanmoo Park, Yongdai Kim

AAAI 2024 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

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

JMLR 2023 A Likelihood Approach to Nonparametric Estimation of a Singular Distribution Using Deep Generative Models Minwoo Chae, Dongha Kim, Yongdai Kim, Lizhen Lin

ICML 2023 Covariate Balancing Using the Integral Probability Metric for Causal Inference Insung Kong, Yuha Park, Joonhyuk Jung, Kwonsang Lee, Yongdai Kim

ICCV 2023 Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge Distillation Dongyoon Yang, Insung Kong, Yongdai Kim

ICML 2023 Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples Dongyoon Yang, Insung Kong, Yongdai Kim

ICML 2023 Masked Bayesian Neural Networks : Theoretical Guarantee and Its Posterior Inference Insung Kong, Dongyoon Yang, Jongjin Lee, Ilsang Ohn, Gyuseung Baek, Yongdai Kim

ICML 2022 Learning Fair Representation with a Parametric Integral Probability Metric Dongha Kim, Kunwoong Kim, Insung Kong, Ilsang Ohn, Yongdai Kim

AAAI 2021 Kernel-Convoluted Deep Neural Networks with Data Augmentation Minjin Kim, Young-geun Kim, Dongha Kim, Yongdai Kim, Myunghee Cho Paik

AISTATS 2020 On Casting Importance Weighted Autoencoder to an EM Algorithm to Learn Deep Generative Models Dongha Kim, Jaesung Hwang, Yongdai Kim

ECML-PKDD 2016 An Online Gibbs Sampler Algorithm for Hierarchical Dirichlet Processes Prior Yongdai Kim, Minwoo Chae, Kuhwan Jeong, Byungyup Kang, Hyoju Chung

JMLR 2012 Consistent Model Selection Criteria on High Dimensions Yongdai Kim, Sunghoon Kwon, Hosik Choi

ICML 2004 Gradient LASSO for Feature Selection Yongdai Kim, Jinseog Kim