Insung Kong

24 papers A* 6Journal 18
YearRankTypeTitle / Venue / Authors
2025 J jnl
Bayesian Additive Regression Trees for functional ANOVA model.
CoRR
Seokhun Park, Insung Kong, Yongdai Kim
2025 J jnl
Bayesian Neural Networks for Functional ANOVA model.
CoRR
Seokhun Park, Choeun Kim, Jihu Lee, Yunseop Shin, Insung Kong, Yongdai Kim
2025 J jnl
Fair Representation Learning for Continuous Sensitive Attributes Using Expectation of Integral Probability Metrics.
IEEE Trans. Pattern Anal. Mach. Intell.
Insung Kong, Kunwoong Kim, Yongdai Kim
2025 J jnl
Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metrics.
CoRR
Insung Kong, Kunwoong Kim, Yongdai Kim
2025 J jnl
Fairness Through Matching.
CoRR
Kunwoong Kim, Insung Kong, Jongjin Lee, Minwoo Chae, Sangchul Park, Yongdai Kim
2025 J jnl
Fairness Through Matching.
Trans. Mach. Learn. Res.
Kunwoong Kim, Insung Kong, Jongjin Lee, Minwoo Chae, Sangchul Park, Yongdai Kim
2025 J jnl
Learning deep generative models based on binomial log-likelihood.
Neurocomputing
Hwichang Jeong, Insung Kong, Yongdai Kim
2025 J jnl
On the Universal Representation Property of Spiking Neural Networks.
CoRR
Shayan Hundrieser, Philipp Tuchel, Insung Kong, Johannes Schmidt-Hieber
2025 J jnl
On the expressivity of deep Heaviside networks.
CoRR
Insung Kong, Juntong Chen, Sophie Langer, Johannes Schmidt-Hieber
2025 J jnl
Posterior Concentrations of Fully-Connected Bayesian Neural Networks with General Priors on the Weights.
J. Mach. Learn. Res.
Insung Kong, Yongdai Kim
2025 J jnl
ReLU integral probability metric and its applications.
CoRR
Yuha Park, Kunwoong Kim, Insung Kong, Yongdai Kim
2025 A* conf
Tensor Product Neural Networks for Functional ANOVA Model.
ICML
Seokhun Park, Insung Kong, Yongchan Choi, Chanmoo Park, Yongdai Kim
2025 J jnl
Tensor Product Neural Networks for Functional ANOVA Model.
CoRR
Seokhun Park, Insung Kong, Yongchan Choi, Chanmoo Park, Yongdai Kim
2024 J jnl
Posterior concentrations of fully-connected Bayesian neural networks with general priors on the weights.
CoRR
Insung Kong, Yongdai Kim
2023 A* conf
Covariate balancing using the integral probability metric for causal inference.
ICML
Insung Kong, Yuha Park, Joonhyuk Jung, Kwonsang Lee, Yongdai Kim
2023 J jnl
Covariate balancing using the integral probability metric for causal inference.
CoRR
Insung Kong, Yuha Park, Joonhyuk Jung, Kwonsang Lee, Yongdai Kim
2023 A* conf
Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge Distillation.
ICCV
Dongyoon Yang, Insung Kong, Yongdai Kim
2023 J jnl
Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge Distillation.
CoRR
Dongyoon Yang, Insung Kong, Yongdai Kim
2023 A* conf
Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples.
ICML
Dongyoon Yang, Insung Kong, Yongdai Kim
2023 A* conf
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference.
ICML
Insung Kong, Dongyoon Yang, Jongjin Lee, Ilsang Ohn, Gyuseung Baek, Yongdai Kim
2023 J jnl
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference.
CoRR
Insung Kong, Dongyoon Yang, Jongjin Lee, Ilsang Ohn, Gyuseung Baek, Yongdai Kim
2022 J jnl
Adaptive Regularization for Adversarial Training.
CoRR
Dongyoon Yang, Insung Kong, Yongdai Kim
2022 A* conf
Learning fair representation with a parametric integral probability metric.
ICML
Dongha Kim, Kunwoong Kim, Insung Kong, Ilsang Ohn, Yongdai Kim
2022 J jnl
Learning fair representation with a parametric integral probability metric.
CoRR
Dongha Kim, Kunwoong Kim, Insung Kong, Ilsang Ohn, Yongdai Kim