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

Adaptive Regularization for Adversarial Training. - Dongyoon Yang; Insung Kong; Yongdai Kim (CoRR, 2022)

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

Covariate balancing using the integral probability metric for causal inference. - Insung Kong; Yuha Park; Joonhyuk Jung; Kwonsang Lee; 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, 2023)

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

Posterior concentrations of fully-connected Bayesian neural networks with general priors on the weights. - Insung Kong; Yongdai Kim (CoRR, 2024)

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

Tensor Product Neural Networks for Functional ANOVA Model. - Seokhun Park; Insung Kong; Yongchan Choi; Chanmoo Park; Yongdai Kim (CoRR, 2025)

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

On the expressivity of deep Heaviside networks. - Insung Kong; Juntong Chen; Sophie Langer; Johannes Schmidt-Hieber (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)