Publications

Journals (Peer Reviewed International)

2023

    • Namgil Lee, Heejung Yang, Hojin Yoo. paper on spectral library generation. (will be updated)

    • Namgil Lee, Heejung Yang, Hojin Yoo. paper on statistical method for chemoproteomics data analysis. (will be updated)

    • Namgil Lee, Heejung Yang, Hojin Yoo. patent on spectral library generation (will be updated)

    • Namgil Lee, Ju-Hyeong Kim. paper on linear mixed models for mass spectrum data analysis (will be updated)

    • Namgil Lee, Ju-Hyeong Kim. paper on network analysis for Korean plant - chemical - toxicity multipartite networks (will be updated)

    • Namgil Lee, Ju-Hyeong Kim. paper on toxicity database for deep learning model-based toxicity prediction (will be updated)

    • Namgil Lee, GangHoo Kim, Sung-Ho Kim. paper on combining graphical model structures. (will be updated)

    • 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*, Heon-Young Yang, Sung-Ho Kim. VARshrink: An R software package for shrinkage estimation for vector autoregressive models. In preparation. Available at CRAN as a reference manual.

2022

  • Namgil Lee, Heejung Yang, Hojin Yoo. patent on semi-supervised learning (will be updated)

  • paper (will be updated)

2021

    • Namgil Lee, Heejung Yang, Hojin Yoo. A surrogate loss function for optimization of $F_\beta$ score in binary classification with imbalanced data. ArXiv Preprint, arXiv:2104.01459, 3 April 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]

    • 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]

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]

    • Jong-Min Kim, Namgil Lee, Sun Young Hwang*. A copula nonlinear Granger causality. Economic Modelling, 88, 420-430, June (2020). [url]

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]

    • 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]

    • 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]

    • 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]

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]

    • Namgil Lee, Jong-Min Kim*. Block tensor train decomposition for missing data estimation. Statistical Papers, 59(4): 1283--1305, December (2018). [url] [preprint] [code]

    • 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]

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]

    • 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]

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]

    • 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]

    • 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]

    • 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]

2015

    • Namgil Lee*, Andrzej Cichocki. Estimating a Few Extreme Singular Values and Vectors for Large-Scale Matrices in Tensor Train Format. SIAM Journal on Matrix Analysis and Applications, 36(3): 994--1014, July (2015). [url] [pdf] [code]

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]

    • 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]

    • 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]

    • Imhoi Koo, Namgil Lee, Rhee Man Kil*. Parameterized cross-validation for nonlinear regression models. Neurocomputing, 71(16--18): 3089--3095, October (2008). [url] [pdf]

Peer Reviewed Proceedings of International Conferences (before 2017)

    • 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.

Others (Patents, etc.)

  • [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.

Other Presentations (before 2018)

    • 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)