Scholar
Insung Kong
Google Scholar ID: NYdp2FQAAAAJ
University of Twente
Citations & Impact
Citations
85
H-index
6
i10-index
3
Publications
16
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- - Submitted Papers:
- - Bayesian Additive Regression Trees for functional ANOVA model (with Seokhun Park, Yongdai Kim)
- - On the use of supervised anomaly detection algorithms for extremely imbalanced data (with Kyungseon Lee, Jongjin Lee, Yongdai Kim)
- - On the expressivity of deep Heaviside networks (with Juntong Chen, Sophie Langer, Johannes Schmidt-Hieber)
- - ReLU integral probability metric and its applications (with Yuha Park, Kunwoong Kim, Yongdai Kim)
- - Publications:
- - Tensor Product Neural Networks for Functional ANOVA Model (ICML 2025)
- - Learning deep generative models based on binomial log-likelihood (Neurocomputing 2025)
- - Posterior concentrations of fully-connected Bayesian neural networks with general priors on the weights (JMLR 2025)
- - Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metrics (TPAMI 2025)
- - Fairness Through Matching (TMLR 2024)
- - On Measuring the Quality of Group Fairness (Journal of Artificial Intelligence Research and Applications 2024)
- - Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge Distillation (ICCV 2023)
- - Covariate balancing using the integral probability metric for causal inference (ICML 2023)
- - Masked Bayesian Neural Networks: Theoretical Guarantee and its Posterior Inference (ICML 2023)
- - Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples (ICML 2023)
- - Learning fair representation with a parametric integral probability metric (ICML 2022)
Research Experience
- - 2024 - present, University of Twente, Enschede, Netherlands, Postdoctoral researcher, Collaborator: Johannes Schmidt-Hieber
Education
- - 2018-2024, Seoul National University, PhD, Statistics, Advisor: Yongdai Kim
- - 2013-2018, Seoul National University, BSc, Statistics
- - 2010-2013, Gyeonggi Science High School
Background
- Research Interests: statistical machine learning and deep learning theory. Currently working on a project funded by the European Research Council that extends mathematics and statistics from artificial neural networks to biological neural networks at the University of Twente.
Co-authors