Research Summary

Research Subjects

• Data Science

Selected Publications

Papers with an asterisk(*) are based on research conducted outside of RIKEN.

• 1.*Toyoura K., Hirano D., Seko A., Shiga M., Kuwabara A., Karasuyama M., Shitara K., and Takeuchi I.:
  "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"
  Physical Review B, 93 054112 (2016).
• 2.*Nakagawa K., Suzumura S., Karasuyama M., Tsuda K., and Takeuchi I.:
  "Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining"
  Proceedings of The 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2016) (2016).
• 3.*Shibagaki A., Karasuyama M., Hatano K., and Takeuchi I.:
  "Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling"
  Proceedings of The 33rd International Conference on Machine Learning (ICML2016) (2016).
• 4.*Takeuchi I., Hongo T., Sugiyama M., and Nakajima S.:
  "Parametric Task Learning"
  Proceedings of The 27th Annual Conference on Neural Information Processing Systems (NIPS2013) (2013).
• 5.*Karasuyama M., Harada N., Sugiyama M., and Takeuchi I.:
  "Multi-parametric solution-path algorithm for instance-weighted support vector machines"
  Machine Learning, 88, 297-330 (2012).
• 6.*Takeuchi I., and Sugiyama M.:
  "Target neighbor consistent feature weighting for nearest neighbor classification"
  Proceedings of The 25th Annual Conference on Neural Information Processing Systems (NIPS2011) (2011).
• 7.*Karasuyama M., and Takeuchi I.:
  "Multiple incremental decremental learning of support vector machines",
  IEEE Transactions on Neural Networks, 21, 1048-1059 (2010).
• 8.*Takeuchi I., Tagawa H., Tsujikawa A., Nakagawa M., Katayama M., Guo Y., and Seto M:
  "The potential of copy number gains and losses, detected by array-based comparative genomic hybridization, for computational differential diagnosis of B-cell lymphomas and genetic regions involved in lymphomagenesis"
  Haematologica-The Hematology Journal, 94, 61-69 (2009).
• 9.*Takeuchi I., Nomura K., and Kanamori T.:
  "Nonparametric conditional density estimation using piecewise-linear solution path of kernel quantile regression"
  Neural Computation, 21, 2, 533-559 (2009).
• 10.*Takeuchi I., Le QV., Sears TD., and Smola AJ:
  "Nonparametric quantile estimation",
  Journal of Machine Learning Research, 7, 1231-1264 (2006).

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