Publications

Books

  1. Noh, Y.-K., Sugiyama, M. (2021 expected) Nearest neighbor algorithms in high dimensions: theory and practice, Cambridge University Press
  2. Editors: Zhang, M. L., Noh, Y.-K. (2017) Proceedings of The 9th Asian Conference on Machine Learning, ACMr 2017, Seoul, Korea, November 15-17, 2017, Proceedings of Machine Learning Research 77, JMLR.org (637 pages)
  3. Author: Sugiyama, M., Translators: Noh, Y.-K., Nam, H., and Kim, E.-S. (2016) Statistical Machine Learning - Pattern Recognition Based on Generative Models (Translation from Japanese into Korean), SNU Press

Journals

  1. Lee, S., Lee, T., Noh, Y.-K., and Kim, S. (2019) Ranked k-Spectrum Kernel for Comparative and Evolutionary Comparison of Exons, Introns, and CpG Islands, IEEE/ACM Transactions on Computational Biology and Bioinformatics (accepted)
  2. Noh, Y.-K., Park, J. Y., Choi, B. G., Kim, K.-E. and Rha, S.-W. (2019) A Machine Learning-Based Approach for the Prediction of Acute Coronary Syndrome Requiring Revascularization, Journal of Medical Systems, 43:253 (in press)
  3. Sugiyama, M. and Noh, Y.-K., (2019) Foreword: special issue for the journal track of the 10th Asian Conference on Machine Learning (ACML 2018), Machine Learning, 108(5):717-719
  4. Choi, B. G., Rha, S.-W., Kim, S. W., Kang, J. H., Park, J. Y., and Noh, Y.-K. (2019) Machine Learning for the Prediction of New-Onset Diabetes Mellitus during 5-Year Follow-up in Non-Diabetic Patients with Cardiovascular Risks, Yonsei Medical Journal, 60(2):191-199
  5. Noh, Y.-K., Hamm, J., Park, F. C., Zhang, B.-T., and Lee, D. D. (2018) Fluid Dynamic Models for Bhattacharyya-based Discriminant Analysis, IEEE Transactions in Pattern Analysis and Machine Intelligence, 40(1):92-105
  6. Noh, Y.-K., Zhang, B.-T., and Lee, D. D. (2018), Generative Local Metric Learning for Nearest Neighbor Classification, IEEE Transactions in Pattern Analysis and Machine Intelligence, 40(1):106-118
  7. Noh, Y.-K., Sugiyama, M., Liu, S., Marthinus, C., Park, F. C., and Lee, D. D. (2018), Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence, Neural Computation, 30(7):1930-1960
  8. Choi B. G., Kim, D. J., Baek, M. J., Ryu, Y. G., Kim, S. W., Lee, M. W., Park, J. Y., Noh, Y.-K., Choi, S. Y., Byun, J. K., Shim, M. S., Mashaly, A., Li, H., Park, Y., Jang, W. Y., Kim, W., Kang, J. H., Choi, J. Y., Park, E. J., Park, S.-H., Lee, S., Na, J. O., Choi, C. U., Kim, E. J., Park, C. G., Seo, H. S., Oh, D. J., Rha, S.-W. (2018) Hyperuricaemia and development of type 2 diabetes mellitus in Asian population, Clinical and Experimental Pharmacology and Physiology, 45(6):499-506
  9. Min, B.-K., Dähne, S., Ahn, .-H., Noh, Y.-K., and Müller, K-.R. (2016) Decoding of top-down cognitive processing for SSVEP-controlled BM Scientific Reports 6, 36267
  10. Sasaki, H., Noh, Y.-K., Niu, G., and Sugiyama, M. (2016) Direct density-derivative estimation, Neural Computation, 28(6):1101-1140
  11. Park, J. Y., Rha, S.-W., Choi, B. G., Choi, S. Y., Choi, J. W., Ryu, S. K., Lee, S. J., Kim, S., Noh, Y.-K., Akkala, R. G., Li, H., Ali, J., Kim, J. B., Lee, S., Na, J. O., Choi, C. U., Lim, H. E., Kim, J. W., Kim, E. J., Park, C. G., Seo, H. S., and Oh, D. J. (2016) Impact of angiotensin converting enzyme inhibitor versus angiotensin receptor blocker on incidence of new-onset diabetes mellitus in Asians, Yonsei Medical Journal, 57(1): 180-186
  12. Noh, Y.-K., Lee, D. D., Yang, K.-A., Kim, C., and Zhang, B.-T. (2015) Molecular Learning with DNA Kernel Machines, Biosystems, 137:73-83
  13. Park, J. Y., Rha, S.-W., Choi, B. G., Choi, J. W., Ryu, S. K., Kim, S., Noh, Y.-K., Choi, S. Y., Akkala, R. G., Li, H., Ali, J., Xu, S., Ngow, H. A., Lee, J. J., Lee, G. N., Kim, J. B., Lee, S., Na, J. O., Choi, C. U., Lim, H. E., Kim, J. W., Kim, E. J., Park, C. G., Seo, H. S., and Oh, D. J. (2015) Impact of low dose atorvastatin on development of new-onset diabetes mellitus in Asian population: Three-year clinical outcomes, International Journal of Cardiology, 184:502-506
  14. Kim, E.-S., Noh, Y.-K., Zhang, B.-T. (2015) Locally Linear Embedding for Face Recognition with Simultaneous Diagonalization, Journal of KIISE: Computing Practices, 21(2):235-241
  15. Park, J. Y., Ryu, S. K., Choi, J. W., Kim, M. M., Jun, J. H., Rha, S.-W., Park, S.-M., Kim, H. J., Choi, B. G., Noh, Y.-K., and Kim., S. (2014) Association of inflammation, myocardial fibrosis and cardiac remodelling in patients with mild aortic stenosis as assessed by biomarkers and echocardiography, Clinical and Experimental Pharmacology and Physiology, 41:185-191
  16. Noh, Y.-K., Park, F. C., Lee, D. D. (2012) Model Dependency of the Performance in Generative Local Metric Learning, Journal of KIISE: Software and Applications, 39(5):347-354
  17. Kim, J. S., Lee, J.-W., Noh, Y.-K., Park, J.-Y., Lee, D.-Y., Yang, K.-A., Chai, Y. G., Kim, J. C., and Zhang, B.-T. (2008) An Evolutionary Monte Carlo Algorithm for Predicting DNA Hybridization, Biosystems, 91(1):69-75

Conferences

  1. Hamm, J. and Noh, Y.-K. (2018), K-Beam Subgradient Descent for Minimax Optimization, International Conference on Machine Learning (ICML)
  2. Ganguly, S., Ryu, J., Kim, Y.-H., Noh, Y.-K., Lee, D. D. (2018) Nearest neighbor density functional estimation based on inverse Laplace transform, arXiv:1805.08342
  3. Jang, C., Noh, Y.-K., and Park, F. C. (2018) Global Geometric Distortion Measures for Manifold Learning, Conference: 2018 15th International Conference on Ubiquitous Robots (UR)
  4. Kim, S., Noh, Y.-K., and Park, F. C. (2018) Transfer Learning and Model Compression for Fast and Accurate Industrial Optical Inspection, Conference: 2018 15th International Conference on Ubiquitous Robots (UR)
  5. Noh, Y.-K., Sugiyama, M., Kim, K.-E., Park, F. C., and Lee, D. D. (2017), Generative Local Metric Learning for Kernel Regression, Advances in Neural Information Processing Systems 30 (NeurIPS)
  6. Kim, S., Kim, W., Noh, Y.-K., and Park, F. C. (2017) Transfer learning for automated optical inspection, International Joint Conference on Neural Networks (IJCNN)
  7. Kim, H., Lee, H., Choi, S., Noh, Y.-K., and Kim, H. J. (2017) Motion Planning with Movement Primitives for Cooperative Aerial Transportation in Obstacle Environment, IEEE International Conference on Robotics and Automation (ICRA)
  8. Sasaki, H., Noh, Y.-K., and Sugiyama, M. (2015) Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation, Eighteenth International Conference on Artificial Intelligence and Statistics (AISTATS)
  9. Kim, H., Lim, W., Lee, K., Noh, Y.-K., Kim, K.-E. (2015) Reward Shaping for Model-Based Bayesian Reinforcement Learning, Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI)
  10. Kim, E.-S., Noh, Y.-K., and Zhang, B.-T. (2014) Locally Linear Embedding for Face Recognition with Simultaneous Diagonalization, Korea Computer Congress <Outstanding Paper Awards, Women in Computer Science Awards>
  11. Noh, Y.-K., Sugiyama, M., Liu, S., Marthinus, C., Park, F. C., and Lee, D. D. (2014) Bias Reduction and Metric Learning for Nearest-Neighbor Estimation of Kullback-Leibler Divergence, Seventeenth International Conference on Artificial Intelligence and Statistics (AISTATS)
  12. Noh, Y.-K., Park, F. C., and Lee, D. D. (2013) k-Nearest Neighbor Classification Algorithm for Multiple Choice Sequential Sampling, Proceedings of the Thirty-Fifth Annual Conference of the Cognitive Science Society (CogSci)
  13. Noh, Y.-K., Park, F. C., and Lee, D. D. (2012) Diffusion Decision Making for Adaptive k-Nearest Neighbor Classification, Advances in Neural Information Processing Systems 25 (NeurIPS)
  14. Lee, Y. S., Noh, Y.-K., Kimberg, D., Coslett, B., and Schwartz, M. (2012) Predicting language performance using multivariate lesion pattern-based analysis, Human Brain Mapping
  15. Noh, Y.-K., Park, F. C., and Lee, D. D. (2011) Model Dependency of the Performance in Generative Local Metric Learning, Korea Computer Congress <Outstanding Paper Awards>
  16. Noh, Y.-K., Lee, D. D., and Park, F. C. (2011) Parametric Model-Based Local Metric Learning for Jensen-Shannon Divergence Estimation, Korea Computer Congress
  17. Noh, Y.-K., Zhang, B.-T., and Lee, D. D. (2010) Generative Local Metric Learning for Nearest Neighbor Classification, Advances in Neural Information Processing Systems 23 (NeurIPS)
  18. Noh, Y.-K., Zhang, B.-T., and Lee, D. D. (2010) Fluid Dynamics Models for Low Rank Discriminant Analysis, Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS)
  19. Noh, Y.-K., Hamm, J., and Lee, D. D. (2008) Regularized Discriminant Analysis for Transformation-Invariant Object Recognition, International Conference on Pattern Recognition (ICPR)
  20. Noh, Y.-K., Kim, C., and Zhang, B.-T. (2007) Design of Temperature Regulation for DNA Kernel to Satisfy Positive Definiteness, Korea Computer Congress
  21. Noh, Y.-K., Kim, C., and Zhang, B.-T. (2006) Modeling of Classifiers by Simple Kernel Update, Korea Computer Congress
  22. Noh, Y.-K., Kim, S.-K., Kim, C., and Zhang, B.-T. (2005) MicroRNA Target Prediction using DNA Kernels, Korea Computer Congress
  23. Noh, Y.-K., Kim, C., and Zhang, B.-T. (2005) Design of Kernels Based on DNA Computing for Concept Learning, Korean Society for Cognitive Science Annual Spring Conference
  24. Noh, Y.-K., Kang, Y.-J., Kim, C., and Zhang, B.-T. (2005) Design of DNA Computing- Based Kernels for Assigning Metric between DNA Sequences, Computational Intelligence

Peer Reviewed Workshop publications

  1. Yoon, S., Song, Y., Kim, M., Park, F. C. and Noh, Y.-K. (2018) Interpretable Feature Selection Using Local Information for Credit Assessment, NeurIPS (NIPS) 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy, Montral, Canada
  2. Noh, Y.-K., Sugiyama, M., and Lee, D. D. (2017) Generative Local Metric Learning for Nearest Neighbor Methods, NeurIPS (NIPS) 2017 Workshop: Nearest Neighbors for Modern Applications with Massive Data, Long Beach, U.S.A
  3. Noh, Y.-K., Sugiyama, M., Kim, K.-E., Park, F. C., and Lee, D. D. (2015) Generative Local Metric Learning for Nadaraya-Watson Kernel Estimation, NeurIPS (NIPS) 2015 Workshop: Nonparametric Methods for Large Scale Representation Learning, Montreal, Canada
  4. Park, J. Y., Noh, Y.-K., Choi, B. G., Rha, S.-W., and Kim, K.-E. (2015) TCTAP A-010 A Machine Learning-Based Approach to Prediction of Acute Coronary Syndrome, Journal of the American College of Cardiology, 65(17):S6
  5. Noh, Y.-K., Park, F. C., Hamm J. and Lee, D. D. (2015) Feature Selection for Robot Learning Using Nearest Neighbor Information, The 14th Advanced Mechanism Control Symposium, The University of Tokyo, Tokyo, Japan
  6. Noh, Y.-K., Park, F. C., Kim, K.-E., and Lee, D. D. (2014) Machine Learning Approach for Sequential Sampling--k-Nearest Neighbor Classification and Metric Learning, Asia-Pacific Conference on Computational Behavioral Sciences (APCCBS), Seoul National University, Korea
  7. Kim, J., Noh, Y.-K., Fific M., and Zhang, B.-T. (2014) Closed-Form Approximation of Drift Diffusion Response Time for Parameter Estimation, Asia-Pacific Conference on Computational Behavioral Sciences (APCCBS), Seoul National University, Korea
  8. Kim, E.-S., Noh, Y.-K., Sugiyama, M., and Zhang B.-T. (2014) Locally Linear Embedding for Face Recognition with Simultaneous Diagonalization, Asia-Pacific Conference on Computational Behavioral Sciences (APCCBS), Seoul National University, Korea
  9. Noh, Y.-K. and Min, B.-K. (2014) Feature Selection for Brain-Computer Interface Using Nearest Neighbor Information, The 2nd IEEE International Winter Workshop on Brain-Computer Interface (BCI), High-One Resort, Korea
  10. Kim, E.-S., Noh, Y.-K., and Zhang, B.-T. (2012) Learning-style recognition from eye-hand movement using a dynamic Bayesian network, NeurIPS (NIPS) 2012 Workshop: Personalizing Education with Machine Learning, Lake Tahoe, NE, U.S.A
  11. Noh, Y.-K., Park, F. C., and Lee, D. D. (2012) Bayesian Diffusion Decision Model for Adaptive k-Nearest Neighbor Classification, Snowbird Learning Workshop, Snowbird, UT, U.S.A
  12. Noh, Y.-K., Park, F. C., Hamm J., and Lee, D. D. (2012) Machine Learning Algorithms for Transformation-Invariant Object Recognition-Regularized Discriminant Analysis, The 11th Advanced Mechanism Control Symposium, The University of Tokyo, Tokyo, Japan
  13. Shi, Y., Noh, Y.-K., Sha, F., and Lee, D. D. (2011) Learning Discriminative Metrics via Generative Models and Kernel Learning, NeurIPS (NIPS) 2011 Workshop: Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity, Granada, Spain
  14. Noh, Y.-K. and Lee, D. D. (2011) Learning Metrics for Nearest Neighbor Classification, Information Theory and Applications, San Diego, CA, U.S.A.
  15. Noh, Y.-K. and Lee, D. D. (2009) Classifying High-Dimensional Data: Bhattacharyya-based Discriminant Analysis, US-Korea Conference, Raleigh, NC, U.S.A.
  16. Noh, Y.-K., Zhang, B.-T., and Lee, D. D. (2009) Kernel Machines Made of DNA Molecules, Learning Workshop, Clearwater, FL, U.S.A.
  17. Noh, Y.-K., Hamm, J., and Lee, D. D. (2009) Perturbation Methods for Discriminant Analysis, Learning Workshop, Clearwater, FL, U.S.A.
  18. Noh, Y.-K., Zhang, B.-T., and Lee, D. D. (2008) Variational Bounds for Discriminant Analysis, Snowbird Workshop, Salt Lake City, UT, U.S.A.
  19. Noh, Y.-K., Zhang, B.-T., and Lee, D. D. (2006) DNA Computing-Based Kernel Machines, MLSS Workshop, National Taiwan University of Sci & Tech, Taipei, Taiwan