Insung Kong
Address
Zilverling (building no. 11), room 2122
Hallenweg 19, 7522 NH Enschede
Netherlands
Address
Zilverling (building no. 11), room 2122
Hallenweg 19, 7522 NH Enschede
Netherlands
I am a postdoctoral researcher at University of Twente, working with Johannes Schmidt-Hieber. Before that, I completed my Ph.D. studies at the Seoul National University under the supervision of Yongdai Kim in 2024.
My research focuses on statistical machine learning, deep learning theory, trustworthy AI, and Bayesian statistics.
Currently, I’m working on a project funded by the European Research Council that extends mathematics and statistics from artificial neural networks to biological neural networks.
2024 - University of Twente, Enschede, Netherlands
Postdoctoral researcher
2018 - 2024 Seoul Natioanal University, Seoul, Republic of Korea
Doctor of Philsosphy, Statistics
Thesis: Posterior Concentration Rates of Bayesian Neural Networks [pdf]
2013-2018 Seoul Natioanal University, Seoul, Republic of Korea
Bachelor of Science, Statistics
2010-2013 Gyeonggi Science High School, Suwon, Republic of Korea
Bayesian Additive Regression Trees for functional ANOVA model [pdf]
Seokhun Park, Insung Kong, Yongdai Kim.
On the use of supervised anomaly detection algorithms for extremely imbalanced data
Kyungseon Lee, Jongjin Lee, Insung Kong, Yongdai Kim.
On the expressivity of deep Heaviside networks [pdf]
Insung Kong, Juntong Chen, Sophie Langer, Johannes Schmidt-Hieber.
ReLU integral probability metric and its applications [pdf]
Yuha Park, Kunwoong Kim, Insung Kong, Yongdai Kim.
Uncertainty Quantification of Group-fair Models via Gibbs Posterior
Jihu Lee, Kunwoong Kim, Insung Kong, Yongdai Kim.
Tensor Product Neural Networks for Functional ANOVA Model [pdf]
Seokhun Park, Insung Kong, Yongchan Choi, Chanmoo Park, Yongdai Kim. International Conference on Machine Learning (ICML). 2025.
Learning deep generative models based on binomial log-likelihood [pdf]
Hwichang Jeong, Insung Kong, Yongdai Kim. Neurocomputing. 2025.
Posterior concentrations of fully-connected Bayesian neural networks with general priors on the weights [pdf]
Insung Kong, Yongdai Kim. Journal of Machine Learning Research (JMLR). 2025.
Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metrics [pdf] [arxiv]
Insung Kong*, Kunwoong Kim*, Yongdai Kim. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). 2025.
Fairness Through Matching [pdf]
Kunwoong Kim, Insung Kong, Jongjin Lee, Minwoo Chae, Sangchul Park, Yongdai Kim. Transaction on Machine Learning Research (TMLR). 2024.
On Measuring the Quality of Group Fairness [pdf]
Kunwoong Kim, Insung Kong, Jongjin Lee, Minwoo Chae, Sangchul Park, Yongdai Kim. Journal of Artificial Intelligence Research and Applications. 2024.
Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge Distillation [pdf]
Dongyoon Yang, Insung Kong, Yongdai Kim. International Conference on Computer Vision (ICCV). 2023.
Covariate balancing using the integral probability metric for causal inference [pdf]
Insung Kong, Yuha Park, Joonhyuk Jung, Kwonsang Lee, Yongdai Kim. International Conference on Machine Learning (ICML). 2023.
Masked Bayesian Neural Networks: Theoretical Guarantee and its Posterior Inference [pdf]
Insung Kong, Dongyoon Yang, Jongjin Lee, Ilsang Ohn, Gyuseung Baek, Yongdai Kim. International Conference on Machine Learning (ICML). 2023.
Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples [pdf]
Dongyoon Yang, Insung Kong, Yongdai Kim. International Conference on Machine Learning (ICML). 2023.
Learning fair representation with a parametric integral probability metric [pdf]
Dongha Kim, Kunwoong Kim, Insung Kong, Ilsang Ohn, Yongdai Kim. International Conference on Machine Learning (ICML). 2022.
Spring 2024 Instructor, Statistical Machine Learning
Sookmyung Women’s University, Republic of Korea
2025-06-13 Deep Heaviside Networks and their Augmentations
The 4th workshop on Mathematics and AI, University of Tilburg, Netherlands
2025-06-05 On the Optimality of Bayesian Neural Networks with Gaussian Priors
Academic Seminar, Ruhr University Bochum, Germany
2025-05-13 Deep Heaviside networks: from artificial neural networks to biological neural networks
Academic Seminar, Inha University, Republic of Korea
2025-05-07 On the Expressivity of Deep Heaviside Networks
Workshop on the Statistical Theory of Neural Networks, Egmond aan Zee, Netherlands
2024-12-27 Function approximations using DNNs and applications for statistical learning
Academic Seminar, Seoul National University, Republic of Korea
2024-06-13 A Study on Bayesian Neural Networks and Their Variations
Academic Seminar, Sookmyung Women’s University, Republic of Korea
2024-06-07 Posterior concentrations of fully-connected Bayesian neural networks with general priors on the weights
Bayesian Statistical Research Society, University of Seoul, Republic of Korea
2024-08-29 Best Ph.D. Dissertation Award from the College of Natural Sciences
Seoul National University, Republic of Korea
Annals of Statistics, Bernoulli, Electronic Journal of Statistics, IEEE Transactions on Information Theory