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Our Researchers

February 22, 2022

Yuzuru Okajima

Automated Reasoning Research Group
Data Science Research Laboratories

Research Domains

  • Machine learning
  • Algorithms for large data

Academic/Career History

  • April 2008 - Present : NEC Corporation
  • April 2006 - March 2008: Master of Engineering, Graduate School of Information Science and Technology, The University of Tokyo, Japan
  • April 2002 - March 2006: Bachelor of Engineering, Faculty of Engineering, The University of Tokyo, Japan

Conference Presentation

  • Yoichi Sasaki, Yuzuru Okajima, "Alternative Ruleset Discovery to Support Black-box Model Predictions", in Proceedings of the 2021 IEEE International Conference on Data Mining (ICDM), December 7-10, 2021, Auckland, New Zealand.
  • Daniel Andrade, Yuzuru Okajima. Efficient Bayes Risk Estimation for Cost-Sensitive Classification. The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019), 2019.
  • Yuzuru Okajima, Kunihiko Sadamasa. Decision List Optimization based on Continuous Relaxation. The SIAM International Conference on Data Mining (SDM19), 2019.
  • Yuzuru Okajima, Kunihiko Sadamasa. Deep neural networks constrained by decision rules. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), January, 2019
  • Yuzuru Okajima and Koichi Maruyama. Random Clustering for Multiple Sampling Units to Speed Up Run-time Sample Generation. DEIM 2018, 2018.
  • Daniel Andrade, Kenji Fukumizu, and Yuzuru Okajima. Convex Feature Clustering and Selection With Class Label Information. Optimization for Machine Learning at NIPS 2017 Workshop, Long Beach, 2017.
  • Yuzuru Okajima, Kouichi Maruyama, “Faster Linear-space Orthogonal Range Searching in Arbitrary Dimensions”, Proceedings of the Meeting on Algorithm Engineering & Expermiments (ALENEX15)
  • Yuzuru Okajima, "Top-k substring matching for auto-completion", Proceedings of the Meeting on Algorithm Engineering & Expermiments (ALENEX14)

Journal

  • Yuzuru Okajima, Koichi Maruyama. Random Clustering for Multiple Sampling Units to Speed Up Run-time Sample Generation. DBSJ Journal, Vol. 17, March, 2019.