Preprint

Published Papers

2021 -
Insung Kong, Kunwoong Kim and Yongdai Kim. (2024). Fair Representation Learning for Continuous Sensitive Attributes using Expectation of Integral Probability Metrics. IEEE Transactions on Pattern Analysis & Machine Intelligence no. 01, pp. 1-12.
Yongchan Choi, Seokhun Park, Chamoo Park, Dongha Kim and Yongdai Kim. (2025). Meta-ANOVA : Screening interactions for interpretable machine learning. J. Korean Stat. Soc.
Seong Kim and Yongdai Kim (2025). Intrinsic Priors for Testing the Equality of Two Zero-inflation Parameters in a Zero-inflated Poisson Distribution. Hacettepe Journal of Mathematics and Statistics (HJMS).
Kunwoong Kim, Insung Kong, Jongjin Lee, Minwoo Chae, Sangchul Park, and Yongdai Kim (2024). Fairness Through Matching. Transactions on Machine Learning Research (TMLR).
Hwichang Jeong, Sehyun Park, June Young Chun, Norio Ohmagari, Yongdai Kim, and Shinya Tsuzuki (2024). Chronological trend of social contact patterns in Japan after the emergence of COVID-19. JIPH.
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2015 - 2020
So Young Ji, et al. (2020). Radiological assessment schedule for high-grade glioma patients after standard treatment. Neuro-Oncology. 23(5), 837-847.
Kim Dongha, Jaesung Hwang and Yongdai Kim (2020). On casting importance weighted auto-encoder to an EM algorithm to learn deep generative models. AISTAT 2020, Italy.
Weissbach, R., Kim, Y., Dorre, A., Fink, A. and Doblhammer, G. (2020). Left-censroed dementia incidences in estimating cohort effect. 27, 38-63, Lifetime Data Analysis.
Jeon, JJ et al. (2020). Learning Multiple Quantiles with Neural Networks. 1238-1248. JCGS.
June Young Chun, Gyuseung Baek and Yongdai Kim (2020). Transmission onset distribution of COVID-19. International Journal of Infectious Diseases,99, 403-407.
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2010 - 2014
Jae Chul Lee, Yongdai Kim, Jae-Won Soh and Byung-Joon Shin, (2014). Risk factors of adjacent segment disease requiring surgery after lumbar spinal fusion. Spine, 39, 339-345.
Dan Shen, Haipeng Shen, Shankar Bhamidi, Yolanda Mu~noz Maldonado, Yongdai Kim and J.S. Marron (2014) Functional Data Analysis of Tree Data Objects. Journal of Computational and Graphical Statistics. 23, 418-438.
Lan Wang, Yongdai Kim and Runze, Li . (2013) Calibrating non-convex penalized regression in ultra-high dimension. Annals of Statistics, 41, 2505-2536.
Chae, Minwoo, Weissbach, Rafael, Cho, Kwang Hyun and Kim, Yongdai (2013), A mixture of beta-Dirichlet processes prior for Bayesian analysis of event history data, Journal of the Korean Statistical Society, volume 42 issue 3 September 2013 313-321.
Kwon, Sunghoon, Kim, Yongdai and Choi, Hosik (2013). Sparse bridge estimation with a diverging number of parameters. Statistics and its Interface. 6, 231-242.
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2000 - 2009
Byoung-Kwon Kim, Jong Won Lee, Pil-je Park, Yong-Sung Shin, Won-Young Lee, Kyung-Ae Lee, Sena Ye, Heesun Hyun, Kyung-Nam Kang, Donghwa Yeo, Youngdai Kim, Sung-Yup Ohn, Dong-Young Noh and Chul-Woo Kim (2009) The multiplex bead array approach to identifying serum biomarkers associated with breast cancer. (2009) Breast Cancer Research. 11(2) R22.
Kim, Yongdai, Kim, Yuwon, Kim, Jinseog, Sangin Lee and Sunghoon Kwon (2009) Boosting on the functional ANOVA decomposition. Statistics and Its Interface. 2. 361-368.
Kim, Yongdai (2009) A Bernstein von Mises theorem for doubly censored data. Statistica Sinica. 19. 581-588.
Kim, Yongdai and Kim, Dohyun. (2009). Bayesian partial likelihood approach for tied observations. Journal of Statistical Planning and Inference. 139(2). 467-477.
Oh, Hee-Seok, Kim, Dongho and Kim, Yongdai (2009). Robust wavelet shrinkage using robust selection of thresholds. Statistics and Computing. 19(1). 27-34.
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- 1999
Kim, Y and Lee, T. (1999) Time and state dependent failure rate model for software reliability. Proceedings of the Joint Statistical meetings. Baltimore, USA.
Kim, Y. et al. (1999) Epidemiology of rota virus diarrhea in Egyptian children and implications for disease control. American Journal of Epidemiology. 150(7). 770-777.
Kim, Y. et al. (1999) Early initiation of breast feeding and the risk of infant diarrhea in rural Egypt. Pediatrics. 104(1). e3-e3.
Kim, Y. (1999) Nonparametric Bayesian estimators for counting processes. Annals of Statistics. 27(2). 562-588.
Kim, Y. and Verducci, J. (1999) Too much sampling kills the UMP test. Statistics and Probability Letters. 41(2), 101-105.
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Books

고학수, 김용대 외 5인 (2021). 인공지능 원론: 설명가능성을 중심으로. 박영사

김용대 (2021). 데이터과학자의 사고법, 김영사

김용대 외 (2020). 4차산업혁명과 미래사회 2, Edited by 연세대학교 경영연구소

이긍희, 김용대, 김기온 (2020). 딥러닝의 통계적 이해, 방송통신대학교

이동수, 권용만, 장인홍, 김세진, 김용대 (2018). R과 빅데이터 이해, 자유아카데미

Refereed Conference Talks

“Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference” at ICML2023, Hawaii, July 2023.

“Improving adversarial robustness by putting more regularizations on less robust samples” at ICML2023, Hawaii, July 2023.

“Covariate balancing using the integral probability metric for causal inference” at ICML2023, Hawaii, July 2023.

“Convex Hull Ensemble Machine” at 2002 IEEE International Conference on Data Mining, Japan, December 2002.

“Learning fair representation with a parametric integral probability” at ICML 2022.

“Kernel-convoluted Deep Neural Networks with Data Augmentation” at The 35th AAAI 2021.

“On casting importance weighted auto-encoder to an EM algorithm to learn deep generative models” at AISTAT 2020, Italy.

“An Online Gibbs Sampler Algorithm for Hierarchical Dirichlet Processes Prior” at ECML-PKDD 2016, Italy, 2016/9/19~23.

“Gradient LASSO for feature selection” at ICML 2004.

“Averaged Boosting: A noise-robust ensemble method” at The Seventh Pacific-Asia Conference on Knowledge Discovery and Data Mining(PAKDD03), Seoul, 2003/4/28~2003/5/1.

“Convex Hull Ensemble Machine” at 2002 IEEE International Conference on Data Mining, Japan, December 2002.

Programs