Insung Kong

According to our database, Insung Kong authored at least 18 papers between 2022 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2026

A Composite Activation Function for Learning Stable Binary Representations.

Seokhun Park, Choeun Kim, Kwanho Lee, Sehyun Park, Insung Kong, Yongdai Kim

CoRR, May, 2026

Hyper Input Convex Neural Networks for Shape Constrained Learning and Optimal Transport.

Shayan Hundrieser, Insung Kong, Johannes Schmidt-Hieber

CoRR, April, 2026

2025

On the Universal Representation Property of Spiking Neural Networks.

Shayan Hundrieser, Philipp Tuchel, Insung Kong, Johannes Schmidt-Hieber

CoRR, December, 2025

Bayesian Neural Networks for Functional ANOVA model.

Seokhun Park, Choeun Kim, Jihu Lee, Yunseop Shin, Insung Kong, Yongdai Kim

CoRR, October, 2025

Bayesian Additive Regression Trees for functional ANOVA model.

Seokhun Park, Insung Kong, Yongdai Kim

CoRR, September, 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., May, 2025

On the expressivity of deep Heaviside networks.

Insung Kong, Juntong Chen, Sophie Langer, Johannes Schmidt-Hieber

CoRR, May, 2025

ReLU integral probability metric and its applications.

Yuha Park, Kunwoong Kim, Insung Kong, Yongdai Kim

CoRR, April, 2025

Fairness Through Matching.

Kunwoong Kim, Insung Kong, Jongjin Lee, Minwoo Chae, Sangchul Park, Yongdai Kim

Trans. Mach. Learn. Res., 2025

Posterior Concentrations of Fully-Connected Bayesian Neural Networks with General Priors on the Weights.

Insung Kong, Yongdai Kim

J. Mach. Learn. Res., 2025

Learning deep generative models based on binomial log-likelihood.

Hwichang Jeong, Insung Kong, Yongdai Kim

Neurocomputing, 2025

Tensor Product Neural Networks for Functional ANOVA Model.

Seokhun Park, Insung Kong, Yongchan Choi, Chanmoo Park, Yongdai Kim

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2023

Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples.

Dongyoon Yang, Insung Kong, Yongdai Kim

Proceedings of the International Conference on Machine Learning, 2023

Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference.

Insung Kong, Dongyoon Yang, Jongjin Lee, Ilsang Ohn, Gyuseung Baek, Yongdai Kim

Proceedings of the International Conference on Machine Learning, 2023

Covariate balancing using the integral probability metric for causal inference.

Insung Kong, Yuha Park, Joonhyuk Jung, Kwonsang Lee, Yongdai Kim

Proceedings of the International Conference on Machine Learning, 2023

Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge Distillation.

Dongyoon Yang, Insung Kong, Yongdai Kim

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

Adaptive Regularization for Adversarial Training.

Dongyoon Yang, Insung Kong, Yongdai Kim

CoRR, 2022

Learning fair representation with a parametric integral probability metric.

Dongha Kim, Kunwoong Kim, Insung Kong, Ilsang Ohn, Yongdai Kim

Proceedings of the International Conference on Machine Learning, 2022