In Preparation
Namgil Lee*. BTT-Soft-Impute: Block tensor train decomposition for missing data estimation using spectral norm regularization. In preparation.
Namgil Lee, Heejung Yang*, Hojin Yoo. Analysis of chemical-gene bipartite network via a user-based collaborative filtering method incorporating chemical structure information. In preparation.
Namgil Lee*, Heejung Yang, Hojin Yoo. A surrogate loss function for optimization of $F_\beta$ score in binary classification with imbalanced data. Under revision. ArXiv Preprint, arXiv:2104.01459, 3 April 2021.
Namgil Lee*, Sung-Ho Kim, Ganghoo Kim. Tentative title: Compatibility testing rules for two independence graphs based on connectivity structure. Submitted (2025-02-09).
Ganghoo Kim, Namgil Lee, Sung-Ho Kim*. A sequential approach for combining model structures of undirected graphical models. Under review (2025-12-02).
Namgil Lee, Heejung Yang*, Hojin Yoo. Regularized Deep Neural Networks for Combining Heterogeneous Features of Peptides in Data Independent Acquisition Mass Spectrometry. Submitted (2026-02-02). [preprint]
Namgil Lee*, Sung-Ho Kim. VARshrink: An R package for shrinkage estimation of high-dimensional vector autoregressive models. Submitted (2026-01-21). An old version is available at CRAN as a reference manual.
2026
Heejun Kang, Jubeen Lee, Hohyun Jung, Namgil Lee*. Predictive Modeling for Drug Repurposing Using Side-Effect and Toxicity Similarities. Journal of the Korean Data Analysis Society, 28(2), to be published., April 2026. (In Korean) KCI
국문 제목: 부작용과 독성 유사도에 기반한 약물 재창출 예측 모델링
Namgil Lee, Jaehyun Park, Yoonjin Lee, Suji Lee, Nakyung Shin, Hohyun Jung*. Latent Space Network Model for the Popularity Effect, with Applications to Bitcoin Networks. Accepted to Journal of Applied Statistics. [url] SCIE
Hojin Yoo, Sang-Jun Han, Jeong-Eun Lee, Chaeyeon Cho, Donggyun Hong, Birang Jeong, Sijin Kim, Go-Yeon Jung, Minjeong Ma, Soeun Jung, Beomjun Park, Namgil Lee, Hee-Seop Yoo, Kwang-Jin Cho, Min-Duk Seo*, Yeonseok Chung*, Byung-Seok Kim*, Heejung Yang*
Discovery of Natural RORγt Inhibitor using Machine Learning, Virtual Screening, and In-vivo Validation. Journal of Advanced Research, Accepted to Journal of Advanced Research. [url] SCIE
2025
Namgil Lee, Hojin Yoo, Juhyoung Kim, Heejung Yang*. A shrinkage-based statistical method for testing group mean differences in quantitative bottom-up proteomics. BMC Bioinformatics, 26, 269. 31 October 2025. [url] SCIE
Namgil Lee*. On the Convergence Rate of Incremental Learning Algorithms for Radial Basis Function Networks. Communications of KNU Research Institute for Mathematical Sciences, 1(1), 31--43, 30 June 2025. [url] ISSN: 3091-7948. (In Korean)
국문 제목: 방사형 기저함수 네트워크를 위한 점진적 학습 알고리즘의 수렴율.
2022
Heejung Yang*, Namgil Lee, Beomjun Park , Jinyoung Park , Jiho Lee , Hyeon Seok Jang, HojinYoo. Hierarchical network analysis of co‑occurring bioentities in literature. Scientific Reports, 12, 7885, 12 May 2022. SCIE
2021
Namgil Lee, Jong-Min Kim*. Dynamic functional connectivity analysis based on time-varying partial correlation with a copula-DCC-GARCH model. Neuroscience Research, 169, 27-39, Aug 2021. [url] SCIE
Namgil Lee, Hojin Yoo, Heejung Yang*. Cluster Analysis of Medicinal Plants and Targets Based on Multipartite Network. Biomolecules, 11(4), 546, 8 April 2021. [url] SCIE
2020
Hohyun Jung, Jae-Gil Lee, Namgil Lee, Sung-Ho Kim*. PTEM: A Popularity-based Topical Expertise Model for Community Question Answering. Annals of Applied Statistics, 14(3), 1304-1325, 18 Sep 2020. [url] SCIE
Jong-Min Kim, Namgil Lee, Sun Young Hwang*. A copula nonlinear Granger causality. Economic Modelling, 88, 420-430, June (2020). [url] SSCI (SCEI급)
2019
Namgil Lee, Jong-Min Kim*. Dynamic functional connectivity analysis of functional MRI based on copula time-varying correlation. Journal of Neuroscience Methods, 323, 32-47, July (2019). [url] SCIE
Namgil Lee, J. W. Choi, H. S. Ko, S. J. Ohh, Y. H. Kim, A. R. Jang, J. S. Kim*. Comparison of linear functions to estimate growth performance and feed intake variations pattern in growing and finishing pigs in high ambient temperature. Journal of the Indonesian Tropical Animal Agriculture, 44(2), 177-186, June (2019). [url] SCOPUS
Jong-Min Kim, Namgil Lee*, Xingyao Xiao. Directional dependence between major cities in China based on copula regression on air pollution measurements. PLoS ONE, 14(3): e0213148, March (2019). [url] [Code is available as supplementary materials] SCIE
Namgil Lee, Jong-Min Kim*. Copula directional dependence for inference and statistical analysis of whole brain connectivity from fMRI data. Brain and Behavior, 9(1): e01191, January (2019). [url] [code] SCIE
2018
Hohyun Jung, Jae-Gil Lee, Namgil Lee, Sung-Ho Kim*. Comparison of fitness and popularity: Fitness-popularity dynamic network model. Journal of Statistical Mechanics-Theory and Experiment, 2018(12): 123403, December (2018). [url] [paper] SCIE
Namgil Lee, Jong-Min Kim*. Block tensor train decomposition for missing data estimation. Statistical Papers, 59(4): 1283--1305, December (2018). [url] [preprint] [code] SCIE
Namgil Lee*, Andrzej Cichocki. Fundamental tensor operations for large-scale data analysis using tensor network formats. Multidimensional Systems and Signal Processing, 29(3): 921--960, July (2018) [url] [preprint] [code] SCIE
2017
Andrzej Cichocki*, Anh-Huy Phan, Qibin Zhao, Namgil Lee, Ivan Oseledets, Masashi Sugiyama and Danilo P. Mandic. Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives. Foundations and Trends® in Machine Learning, 9(6): 431--673, 30 May (2017). [url] [preprint] SCOPUS/ESCI
Tatsuya Yokota*, Namgil Lee, Andrzej Cichocki. Robust multilinear tensor rank estimation using higher order singular value decomposition and information criteria. IEEE Transactions on Signal Processing, 65(5): 1196--1206, March 1, (2017). [url] [preprint] [code] SCIE
2016
Andrzej Cichocki*, Namgil Lee, Ivan Oseledets, Anh-Huy Phan, Qibin Zhao and Danilo P. Mandic. Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions. Foundations and Trends® in Machine Learning, 9(4-5): 249--429, 19 December (2016). [url] [preprint] [code] SCOPUS/ESCI
Namgil Lee, Ah-Young Kim, Chang-Hyun Park, Sung-Ho Kim*. An Improvement on Local FDR Analysis Applied to Functional MRI Data. Journal of Neuroscience Methods, 267: 115--125, July (2016). [url] [preprint] [code] SCIE
Namgil Lee, Hyemi Choi, Sung-Ho Kim*. Bayes shrinkage estimation for high-dimensional VAR models with scale mixture of normal distributions for noise. Computational Statistics and Data Analysis, 101, 250--276, September (2016). [url] [preprint] [code] SCIE
Namgil Lee*, Andrzej Cichocki. Regularized Computation of Approximate Pseudoinverse of Large Matrices Using Low-Rank Tensor Train Decompositions. SIAM Journal on Matrix Analysis and Applications, 37(2): 598--623, May (2016). [url] [pdf] [code] SCIE
2015
2013 and before
Fayyaz Ahmad, Namgil Lee, Eunwoo Kim, Sung-Ho Kim, HyunWook Park*. A shrinkage method for causal network detection of brain regions. International Journal of Imaging Systems and Technology, 23(2): 140--146, June (2013). [url] [pdf] SCIE
Fayyaz Ahmad, Muhammad Maqbool*, Namgil Lee. Regularization of voxelwise autoregressive model for analysis of functional magnetic resonance imaging data. Concepts in Magnetic Resonance Part A, 38A(5): 187--196, September (2011). [url] [pdf] SCIE
Namgil Lee, Jong-Min Kim*. Conversion of categorical variables into numerical variables via Bayesian network classifiers for binary classifications. Computational Statistics and Data Analysis, 54(5): 1247--1265, May (2010). [url] [preprint] [code] SCIE
Imhoi Koo, Namgil Lee, Rhee Man Kil*. Parameterized cross-validation for nonlinear regression models. Neurocomputing, 71(16--18): 3089--3095, October (2008). [url] [pdf] SCIE
Namgil Lee. Block tensor train decomposition for missing data estimation. Proceedings of the KCC2025 (In print).
Namgil Lee, Anh-Huy Phan, Fengyu Cong, Andrzej Cichocki. Nonnegative tensor train decompositions for multi-domain feature extraction and clustering. In: H. Akira, O. Seiichi, K. Doya, I. Kazushi, L. Minho, L. Derong (Eds.) Proceedings of the 23rd International Conference on Neural Information Processing (ICONIP 2016), LNCS vol. 9949, pp. 87--95, Springer, 2016. (Excellent Paper Award)
Namgil Lee, Andrzej Cichocki. Big data matrix singular value decomposition based on low-rank tensor train decomposition. In: Z. Zeng, Y. Li, I. King (Eds.) Advances in Neural Networks - ISNN 2014, LNCS vol. 8866, pp. 121--130, Springer, 2014.
Namgil Lee, Heejung Yang, Hojin Yoo. Chexmix (A detail will be updated)
Namgil Lee, Heejung Yang, Hojin Yoo. Spectral library generation (will be updated)
[Patent Application] 출원번호 제10-2021-0054316호(발명의 명칭: 화합물 생체활성 예측을 위한 준지도 학습 방법 및 그 시스템, SEMI-SUPERVISED TRAINING METHOD FOR BIOACTIVITY PREDICTION OF COMPOUNDS AND SYSTEM THEREOF). 강원대학교 산학협력단, 바이온사이트. Yang Heejung, Yoo Hojin, Lee Namgil*. 27 April 2021.
Namgil Lee, Jong-Min Kim. Block tensor train decomposition for missing data estimation. Date: 2018.06.23. At: The Ninth International Workshop on Simulation (IWS 2018), June 25-29, 2018. The UPC Campus of Polytechnic University of Catalonia, Barcelona, Span. Type: Oral presentation.
Namgil Lee. Robust multilinear rank estimation for tensor regression. Date: 2018.06.21. At: The 2nd International Conference on Econometrics and Statistics (EcoSta 2018), June 19-21, 2018. City University of Hong Kong, Hong Kong. Type: Oral presentation.
Namgil Lee, Sung-Ho Kim. Shrinkage estimation and model selection for vector autoregressive models with an application to fMRI data analysis. Date: 2018.05.26. At: The Korean Statistical Society Spring Conference 2018, May 25-26, 2018. Pusan National University, Busan, South Korea. Type: Oral presentation.
Namgil Lee. Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Date: 2018.02.26. At: 2018 Conference of The Gangwon-Gyeonggi-Incheon Branch of The Korean Statistical Society. Hankuk University of Foreign Studies, Global Campus, Yongin, Gyeonggi-do, South Korea. Oral presentation.
Namgil Lee. Era of Big Data Processing: Challenges and Progresses in Tensor Methods. Date: 2017.11.10. At: NAVER Seminar. NAVER Corporation, Green Factory, Seongnam, Gyeonggi-do, South Korea. Type: Oral presentation
Namgil Lee, Tetsuya Yokota, Andrzej Cichocki. Multilinear rank selection for denoising and dimensionality reduction of multiway data. Date: 2016.05.21. At: The Korean Statistical Society Spring Conference 2016, May 20-21, 2016. Kyungpook National University, Daegu, South Korea. Type: Oral presentation.
Namgil Lee and Andrzej Cichocki. Tensor Train Decompositions for Higher Order Regression with LASSO Penalties. Date: 2016.01.18. At: Workshop on Tensor Decompositions and Applications (TDA 2016), Jan 18-22, 2016. Leuven Institute for Ireland (Irish College), Leuven, Belgium (International). Type: Oral presentation.
Namgil Lee and Andrzej Cichocki. Low-Rank Tensor Networks for Large-Scale Optimization Problems: Future Perspective and Challenges. Date: 2015.06.12. At: Workshop on Low-rank Optimization and Applications, June 8-12, 2015. Hausdorff Center for Mathematics, University of Bonn, Germany (International). Type: Oral presentation. (Invited Talk)
Namgil Lee and Sung-Ho Kim. Empirical Bayes approach to shrinkage estimation for vector autoregressive models. Date: 2011.05.28. At: The Korean Statistical Society 2011 Spring Conference. KAIST, Daejeon, South Korea (Domestic). Type: Oral presentation.
Namgil Lee and Jong-Min Kim. Conversion of categorical variables into numerical variables. Date: 2010.05.21. At: The Korean Statistical Society 2010 Spring Conference. Statistics Center, Daejeon, South Korea (Domestic). Type: Poster presentation. (Best Poster Presentation Award)