Conference proceedings (refereed)

Journal (refereed)


Book authorship

  • Tsuyoshi Ide, Introduction to Anomaly Detection using Machine Learning A Practical Guide with R, Corona Publishing, 2015.
  • Tsuyoshi Ide and Masashi Sugiyama, Anomaly Detection and Change Detection, Kodansha Scientific, 2015.

Book chapters

  • Tsuyoshi Ide, Formalizing expert knowledge through machine learning. In S. K. Kwan, J. C. Spohrer, and Y. Sawatani, ed., Global Perspectives on Service Science: Japan, pp.157-175, Springer, 2016.
  • Tsuyoshi Ide, Change detection from heterogeneous data sources. In Katsutoshi Yada, ed., Data Mining for Service. Springer Verlag, pp.221-243, 2014.

Book editorship

  • Hattori, T. Kawamura, Tsuyoshi Ide, M. Yokoo, and Y. Murakami, editors. New Frontiers in Artificial Intelligence: JSAI 2008 Conference and Workshops, Revised Selected Papers, volume 5447 of Lecture Notes in Artificial Intelligence. Springer Verlag, 2009.

Book translation

  • Sugiyama, Tsuyoshi Ide, T. Kamishima, T. Kurita, and E. Maeda
    The Elements of Statistical Learning (Japanese translation). Kyoritsu, 2014.
  • Tsuyoshi Ide. Chap. 12, Continuous Latent Variables. In H. Motoda, T. Kurita, T. Higuchi, Y. Matsumoto, and N. Murata, ed., Pattern Recognition and Machine Learning. Maruzen, 2006.

Invited Talks

  • Towards consumable analytics: Challenges and recent advances,”
    • IEEE International Workshop on Data Mining for Service (DMS 2015, November 14, 2015), Atlantic City, USA [link, slides].
  • Formalizing expert knowledge through machine learning,”
    • Big Data in Service (March 12, 2014), New York, USA.
  • Formalizing expert knowledge through machine learning,”
  • Historical Perspectives towards Analytics Revolution,”
    • The 56th Annual Symposium of the Institute of Systems, Control and Information Engineers (SCI 12, May 21-23, 2012), Kyoto, Japan [link (in Japanese)].
  • Trajectory regression on networks,”
    • Japanese-French Frontiers of Science Symposium (JFFoS, January 19-22, 2012), Nice, France [slides].
  • Machine Learning for Anomaly Detection and Risk Analysis, II,”
    • IBISML Tutorial (January 12, 2012), Tokyo, Japan.
  • Solving real business problems with math sciences,”
    • SIG Service Science, The Operating Research Society of Japan (December 19, 2011), Tsukuba, Japan.
  • Anomaly detection using sparse structure learning,”
    • Adachi Lab Seminar, Keio University (December 12, 2010), Tokyo, Japan.
  • On recent advances in machine learning for system identification,”
    • DoE Conference 2010 (November 15, 2010), Tokyo, Japan.
  • On the trajectory regression problem on networks,”
    • Sugiyama Lab. Seminar, Department of Computer Science, Tokyo Institute of Technology (October 7, 2010), Tokyo, Japan.
  • On a regression problem for path cost,”
    • ERATO Seminar, Hokkaido University (September 27, 2010), Sapporo, Japan.
  • Detecting Anomalies from Latent Graph Structures,”
    • The 1st Workshop on Latent Dynamics (LD-1, Jun 16, 2010), Tokyo, Japan.
  • On the problem of cost estimation for paths,”
    • Mathematical Informatics Colloquium, University of Tokyo (February 24, 2010), Tokyo, Japan.
  • Applying Machine Learning Techniques to Sensor Data Analysis,”
    • Global COE `CompView’ Kickoff Event, Tokyo Institute of Technology (December 13, 2007), Tokyo, Japan.
  • Why does subsequence time-series clustering produce sine waves?,”
    • Departmental Colloquium, Max Planck Institute for Biological Cybernetics (September 13, 2006), Tübingen, Germany.
  • A Spectral Approach to Anomaly Detection in Computer Systems,”
    • Scientific Computing Seminar, Berkeley Lab (April 25, 2005), Berkeley, USA.
  • Feature Extraction and Anomaly Detection in Web-based Computer Systems,”
    • The Seventh Workshop on Information-Based Induction Sciences (IBIS2004, November 8 -10, 2004), Tokyo, Japan.

Patents (as of Dec. 2016)

Granted US patents

  1. US 9,495,330, “Anomaly detection method, program, and system”
  2. US 9,354,381, “Information processing apparatus, calculation method, program, and storage medium”
  3. US 9,329,329, “Information processing apparatus, calculation method, program, and storage medium”
  4. US 9,317,804, “Calculating risk assessment value of event sequence”
  5. US 8,983,890, “Calculating risk assessment value of event sequence”
  6. US 8,682,633, “Cost evaluation and prediction”
  7. US 8,640,015, “Anomaly detection based on directional data”
  8. US 8,600,721, “Cost evaluation and prediction”
  9. US 8,595,155, “Kernel regression system, method, and program”
  10. US 8,405,551, “Location estimation system, method and program”
  11. US 8,138,974, “Location estimation system, method and program”
  12. US 7,849,124, “Method and system for detecting difference between plural observed results”
  13. US 7,720,640, “Diagnostic data detection and control”
  14. US 7,702,714, “Pairwise symmetry decomposition method for generalized covariance analysis”
  15. US 7,647,524, “Anomaly detection”
  16. US 7,529,991, “Scoring method for correlation anomalies”
  17. US 7,493,361, “Computer operation analysis”
  18. US 7,483,934, “Methods involving computing correlation anomaly scores”
  19. US 7,475,052, “Malfunction condition judgment apparatus, control method, automobile and program method”
  20. US 7,406,653, “Anomaly detection based on directional data”
  21. US 7,346,803, “Anomaly detection”
  22. US 7,181,365, “Diagnostic data detection and control”
  23. US 6,865,325, “Discrete pattern, apparatus, method, and program storage device for generating and implementing the discrete pattern”
  24. US 6,754,419, “Discrete pattern”

Granted Japanese patents

See the Japanese page.