I am studying about statistical methods to analyze various data. My major interests are in machine learning, statistical modeling, numerical optimization, bioinformatics, and systems biology.

Affiliation

Associate Professor
Department of Computer Science
Nagoya Institute of Technology
karasuyama[at mark]nitech.ac.jp
nips09

Publications

Journal Papers

  • M. Gönen*, B. A Weir*, G. S Cowley*, Y. Guan*, A. Jaiswal*, M. Karasuyama*, V. Uzunangelov*, F. Vazquez*, T. Wang*, A. Airola, A. Bivol, J. Boehm, K. Bunte, D. Carlin, S. Chopra, A. Deran, K. Ellrott, P. Gopalacharyulu, K. Graim, B. Hoff, S. Howell, S. Kaski, S. A Khan, D. Marbach, Y. Newton, T. C Norman, S. Ng, T. Pahikkala, E. Paull, A. Sokolov, H. Tang, J. Tang, A. Tsherniak, K. Wennerberg, Y. Xie, X. Zhan, F. Zhu, Broad-DREAM Community, T. Aittokallio, H. Mamitsuka, D. Root, J. M Stuart, G. Xiao, G. Stolovitzky, W. C Hahn & A. A Margolin, (*: equal contribution), A Community-based Challenge for Building Predictive Models of Gene Essentialities Over a Large-scale Functional Screening of Cancer Cell Lines, Cell Systems, (IF in 2016 = 8.406), to appear.
  • S. Suzumura, K. Ogawa, M. Sugiyama. M. Karasuyama, I. Takeuchi, Homotopy Continuation Approaches for Robust SV Classification and Regression, Machine Learning (IF in 2015 = 1.719), vol.106, no.7, pp.1009-1038, 2017.
  • M. Karasuyama, and H. Mamitsuka, Adaptive Edge Weighting for Graph-Based Learning Algorithms, Machine Learning (IF in 2015 = 1.719), vol.106, no.2, pp.307-335, 2017.
  • S. Yotsukura, M. Karasuyama, I. Takigawa, and H. Mamitsuka, Exploring Phenotype Patterns of Breast Cancer within Somatic Mutations: A Modicum in the Intrinsic Code, Briefings in Bioinformatics (IF in 2015 = 8.399), 2016.
  • K. Toyoura, D. Hirano, A. Seko, M. Shiga, A. Kuwabara, M. Karasuyama, K. Shitara, and I. Takeuchi, A Machine Learning-based Selective Sampling Procedure for Identifying Low Energy Region in a Potential Energy Surface: A Case Study on Proton Conduction in Oxides, Physical Review B (IF in 2015 = 3.718), vol.93, no.5, pp.054112, 2016.
  • M. Karasuyama and H. Mamitsuka, Multiple Graph Label Propagation by Sparse Integration, IEEE Transactions on Neural Networks and Learning Systems (IF in 2012 = 3.766), vol.24. no.12, pp.1999-2012, 2013.
  • M. Karasuyama and M. Sugiyama, Canonical Dependency Analysis based on Squared-loss Mutual Information, Neural Networks (IF in 2011 = 2.182), vol.34, pp.46-55, 2012.
  • M. Karasuyama, N. Harada, M. Sugiyama and I. Takeuchi, Multi-parametric Solution-path Algorithm for Instance-weighted Support Vector Machines, Machine Learning (IF in 2010 = 1.967), vol.88, no.3, pp.297-330, 2012.
  • M. Karasuyama and I. Takeuchi, Nonlinear Regularization Path for Quadratic Loss Support Vector Machines, IEEE Transactions on Neural Networks (IF in 2010 = 2.633), vol.22, no.10, pp.1613-1625, Oct. 2011.
  • M. Karasuyama and I. Takeuchi, Multiple Incremental Decremental Learning of Support Vector Machines, IEEE Transactions on Neural Networks (IF in 2010 = 2.633), vol.21, no.7, pp.1048-1059, July 2010.
  • M. Karasuyama, I. Takeuchi and R. Nakano, Efficient Leave-m-out Cross-Validation of Support Vector Regression by Generalizing Decremental Algorithm, New Generation Computing (IF in 2009 = 0.364), vol. 27, no. 4, Special Issue on Data-Mining and Statistical Science, pp.307-318, 2009.
  • H. Moriguchi, I. Takeuchi, M. Karasuyama, S. Horikawa, Y. Ohta, T. Kodama, and H. Naruse, Adaptive kernel quantile regression for anomaly detection, Journal of Advanced Computational Intelligence and Inteligent Informatics, vol.13, no.3, pp.230-236, 2009.

Refereed International Conference Papers

  • K. Nakagawa, S. Suzumura, M. Karasuyama, K. Tsuda, and I. Takeuchi, Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.1785--1794, San Francisco, USA, Aug. 13-17, 2016.
  • A. Shibagaki, M. Karasuyama, K. Hatano, I. Takeuchi, Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling, Proceedings of The 33rd International Conference on Machine Learning (ICML 2016), pp.1577-1586, New York, USA, Jun. 19-24, 2016.
  • A. Shibagaki, Y. Suzuki, M. Karasuyama, I. Takeuchi, Regularization Path of Cross-Validation Error Lower Bounds Advances in Neural Information Processing Systems (NIPS) 28, pp.1666-1674, 2015.
  • M. Karasuyama and H. Mamitsuka, Manifold-based Similarity Adaptation for Label Propagation, Advances in Neural Information Processing Systems (NIPS) 26, pp.1547-1555, 2013. [Link] [Code]
  • M. Karasuyama, N. Harada, M. Sugiyama and I. Takeuchi, Multi-parametric Solution-path Algorithm for Instance-weighted Support Vector Machines, Machine Learning for Signal Processing (MLSP), 2011 IEEE International Workshop on, vol.xx, no.xx, pp.xx-xx, 18-21 Sept. 2011.
  • M. Karasuyama, and I. Takeuchi, Suboptimal Solution Path Algorithm for Support Vector Machine, In L. Getoor and T. Scheffer eds., Proceedings of the 28th International Conference on Machine Learning (ICML), pp. 473-480, June, 2011.
  • M. Karasuyama, and I. Takeuchi, Nonlinear Regularization Path for the Modified Huber loss Support Vector Machines, In International Joint Conference on Neural Networks (IJCNN), pp. 3099-3106, 2010.
  • M. Karasuyama and I. Takeuchi, Multiple Incremental Decremental Learning of Support Vector Machines, In Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta eds., Advances in Neural Information Processing Systems (NIPS) 22, pp. 907-915. 2009.
  • M. Karasuyama, I. Takeuchi and R. Nakano, Reducing SVR Support Vectors by Using Backward Deletion, In Proceedings of the 12th international Conference on Knowledge-Based intelligent information and Engineering Systems (KES), Part III, Lecture Notes In Artificial Intelligence, vol. 5179. Springer-Verlag, Berlin, Heidelberg, pp. 76-83, 2008
  • M. Karasuyama, and R. Nakano, Optimizing Sparse Kernel Ridge Regression Hyperparameters Based on Leave-one-out Cross-validation, In International Joint Conference on Neural Networks (IJCNN), pp. 3463-3468, 2008.
  • M. Karasuyama, and R. Nakano, Optimizing SVR Hyperparameters via Fast Cross-Validation using AOSVR, In International Joint Conference on Neural Networks (IJCNN), pp. 1186-1191, 2007.
  • M. Karasuyama, D. Kitakoshi and R. Nakano, Revised Optimizer of SVR Hyperparameters Minimizing Cross-Validation Error, In International Joint Conference on Neural Networks (IJCNN), pp. 711-718, 2006.

Biography

Education

  • Mar. 2006, Bachelor of Engineering, Nagoya Institute of Technology.
  • Mar. 2008, Master of Engineering, Nagoya Institute of Technology.
  • Mar 2011, Doctor of Engineering, Nagoya Institute of Technology.

Job Experience

  • Apr. 2010 to Mar. 2011, JSPS Research Fellow (DC2)
  • Apr. 2011 to Dec. 2011, JSPS Research Fellow (PD)/Postdoctoral Fellow, Tokyo Institute of Technology
  • Jan. 2012 to Mar. 2015, Assistant Professor, Bioinformatics Center, Institute for Chemical Research, Kyoto University
  • Apr. 2015 to Present, Assistant Professor, Department of Computer Science, Nagoya Institute of Technology