KARASUYAMA Masayuki

写真a

Affiliation Department etc.

Department of Computer Science
Department of Computer Science

Title

Associate Professor

Graduating School

  •  
    -
    2006.03

    Nagoya Institute of Technology   Faculty of Engineering   Department of Computer Science   Graduated

Graduate School

  •  
    -
    2011.03

    Nagoya Institute of Technology  Graduate School, Division of Engineering  Scientific and Engineering SimulationDoctor's Course  Completed

  •  
    -
    2008.03

    Nagoya Institute of Technology  Graduate School, Division of Engineering  Scientific and Engineering SimulationMaster's Course  Completed

External Career

  • 2012.01
    -
    2015.03

    Kyoto University   Intitute for Chemical Research, Bioinformatics Center   Assistant Professor  

  • 2011.04
    -
    2011.12

    Tokyo Institute of Technology   Special researcher of the Japan Society for the Promotion of Science  

 

Papers

  • Fast and Scalable Prediction of Local Energy at Grain Boundaries: Machine-learning based Modeling of First-principles Calculations

    T. Tamura, M. Karasuyama, R. Kobayashi, R. Arakawa, Y. Shiihara, and I. Takeuchi

    Modelling and Simulation in Materials Science and Engineering     2017.08  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

  • Homotopy Continuation Approaches for Robust SV Classification and Regression

    S. Suzumura, K. Ogawa, M. Sugiyama, M. Karasuyama, I. Takeuchi

    Machine Learning     2017.07  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

  • Adaptive edge weighting for graph-based learning algorithms

    M. Karasuyama, H. Mamitsuka

    Machine Learning     2017.02  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

  • A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines

    M. Gönen, B. A Weir, G. S Cowley, Y. Guan, A. Jaiswal, M. Karasuyama, V. Uzunangelov, F. Vazquez, T. Wang, et al.

    Cell Systems     2017  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

  • Machine-learning-based selective sampling procedure for identifying the low-energy region in a potential energy surface: A case study on proton conduction in oxides

    K. Toyoura, D. Hirano, A. Seko, M. Shiga, A. Kuwabara, M. Karasuyama, K. Shitara, I. Takeuchi

    Physical Review B     2016.02  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

  • Exploring Phenotype Patterns of Breast Cancer within Somatic Mutations: A Modicum in the Intrinsic Code

    S. Yotsukra, M. Karasuyama, I. Takigawa, H. Mamitsuka

    Briefings in Bioinformatics     2016  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

  • Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling

    A. Shibagaki, M. Karasuyama, K. Hatano, I. Takeuchi

    Proceedings of The 33rd International Conference on Machine Learning     2016  [Refereed]

    Research paper (international conference proceedings)   Multiple Authorship

  • Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining

    K. Nakagawa, S. Suzumura, M. Karasuyama, K. Tsuda, I. Takeuchi

    Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining     2016  [Refereed]

    Research paper (international conference proceedings)   Multiple Authorship

  • Multiple Graph Label Propagation by Sparse Integration

    M. Karasuyama, H. Mamitsuka

    IEEE Transactions on Neural Networks and Learning Systems     2013  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

  • Canonical Dependency Analysis based on Squared-loss Mutual Information

    M. Karasuyama, M. Sugiyama

    Neural Networks     2012  [Refereed]

    Research paper (scientific journal)   Multiple Authorship

display all >>

Books

  • Big Data Analytics in Genomics

    K.-C. Wong (ed.), S. Yotsukura, M. Karasuyama, I. Takigawa, H. Mamitsuka, et al. (Part: Allotment Writing ,  A Bioinformatics Approach for Understanding Genotype-Phenotype Correlation in Breast Cancer (chapter13) )

    Springer  2016.11

Presentations

  • Cost-sensitive Bayesian optimization for multiple objectives and its application to material science

    2017.06  -  2017.06 

  • Statistical Prediction of Grain-Boundary Properties

    T. Tamura, R. Arakawa, M. Karasuyama, R. Kobayashi, S. Ogata

    The 9th Pacific Rim International Conference of Advanced Materials and Processing  2016.08  -  2016.08 

  • Statistical Prediction of Grain-boundary Properties

    T. Tamura, R. Arakawa, M. Karasuyama, R. Kobayashi, S. Ogata

    The third International Symposium on Atomistic Modeling for Mechanics and Multiphysics of Materials  2016.06  -  2016.06 

  • Regularization Path of Cross-Validation Error Lower Bounds

    A. Shibagaki, Y. Suzuki, M. Karasuyama, I. Takeuchi

    Advances in Neural Information Processing Systems (NIPS)  2015.12  -  2015.12 

  • Manifold-based Similarity Adaptation for Label Propagation

    M. Karasuyama, H. Mamitsuka

    Advances in Neural Information Processing Systems (NIPS)   2013  -  2013 

  • Suboptimal Solution Path Algorithm for Support Vector Machine

    M. Karasuyama, I. Takeuchi

    International Conference on Machine Learning (ICML)  2011  -  2011 

  • Multi-parametric Solution-path Algorithm for Instance-weighted Support Vector Machines

    M. Karasuyama, N. Harada, M. Sugiyama, I. Takeuchi

    IEEE International Workshop on Machine Learning for Signal Processing (MLSP)  2011  -  2011 

  • Nonlinear Regularization Path for the Modified Huber loss Support Vector Machines

    M. Karasuyama, I. Takeuchi

    International Joint Conference on Neural Networks (IJCNN)  2010  -  2010 

  • Multiple Incremental Decremental Learning of Support Vector Machines

    M. Karasuyama, I. Takeuchi

    Advances in Neural Information Processing Systems (NIPS)  2009  -  2009