Publications

• DBLP’s publication list for a nicely formatted summary of my computer science papers.
• Google Scholar Page for the number of citations.
• KDnuggets article on the ranking of data mining conferences.

Conference proceedings (refereed)

• “Convergence-Guaranteed Elastic Net Graphical Model Estimation with Applications to Anomaly Localization,”
  • Dzung Phan, Matt Menickelly,  Tsuyoshi Idé, and Jayant Kalagnanam,
  • In Proceedings of the SIAM International Conference on Data Mining (SDM25; May 1 – 3, 2025, Alexandria, Virginia, USA)
• “TBD,”
  • K. Miyaguchi, M. Joko, T. Idé,
  • SEMI Advanced Semiconductor Manufacturing Conference (ASMC 2025; May 2025, Albany, New York).
• “TBD,”
  • K. Miyaguchi, M. Joko, T. Idé,
  • SEMI Advanced Semiconductor Manufacturing Conference (ASMC 2025; May 2025, Albany, New York).
• “Impact of Lot Arrival Density Fluctuations on Cycle Time Control,”
• 
  • M. Joko, K. Miyaguchi, T. Idé
  • SEMI Advanced Semiconductor Manufacturing Conference (ASMC 2025; May 2025, Albany, New York).
• “Decentralized Collaborative Learning Framework with External Privacy Leakage Analysis,”
  •  Tsuyoshi Idé, Rudy Raymond, Dzung T. Phan,
  • In  JPS Conference Proceedings 43 (2024) 011001 (Proceedings of Blockchain Kaigi 2023 (BCK23)) [ArXiv, doi].
• “Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes,”
  • Dongxia Wu, Tsuyoshi Idé, Georgios Kollias, Jiri Navratil, Aurelie Lozano, Naoki Abe, Yian Ma, and Rose Yu,
  • In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024, May 2-4, 2024, Valencia, Spain), pp. 415-423 [GitHub, ArXiv].
• “Generative Perturbation Analysis for Probabilistic Black-Box Anomaly Attribution,”
  • Tsuyoshi Idé, Naoki Abe
  • In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023, August 6-10, 2023, Long Beach, California, USA), pp.845-856 [GitHub, slides, poster, 2min intro video]
• “Direction aware positional and structural encoding for directed graph neural networks,”
  • Yonas Sium, Georgios Kollias, Tsuyoshi Idé, Payel Das, Naoki Abe, Aurelie Lozano, Qi Li,
  • In Proceedings of the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023, June 4-10, 2023, Rhodes Island, Greece), pp.1-5 [slides, poster, IEEE].
• “Directed Graph Auto-Encoders,”
  • Georgios Kollias, Vasileios Kalantzis, Tsuyoshi Idé, Aurelie Lozano, Naoki Abe,
  • In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 22, February 22-March 1, 2022, virtual), pp.7211–7219 [poster].
• “Cardinality-Regularized Hawkes-Granger Model,”
  • Tsuyoshi Idé, Georgios Kollias, Dzung T. Phan, Naoki Abe,
  • Advances in Neural Information Processing Systems 34 (NeurIPS 21, Dec 6-14, 2021, virtual), pp.2682-2694 [GitHub, slides, poster, arXiv].
• “Decentralized Collaborative Learning with Probabilistic Data Protection,”
  • Tsuyoshi Idé, Rudy Raymond,
  • In Proceedings of the 2021 IEEE International Conference on Smart Data Services (SMDS 21, September 5-10, 2021, virtual), pp.234-243 [slides, arXiv, IEEE Xplore].
• “Anomaly Attribution with Likelihood Compensation,”
  • Tsuyoshi Idé, Amit Dhurandhar, Jiri Navratil, Moninder Singh, Naoki Abe,
  • In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 21, February 2-9, 2021, virtual), pp.4131-4138 [GitHub, slides, poster, official version, arXiv, IBM product].
• “Predicting Nocturnal Hypoglycemia under Free-Living Conditions from Continuous Glucose Monitoring Data with Extended Prediction Horizon,”
  • Long Vu, Sarah Kefayati, Tsuyoshi Idé, Venkata Pavuluri, Gretchen Jackson, Lisa Latts, Yuxiang Zhong, Pratik Agrawal, Yuan-chi Chang,
  • In AMIA Annual Symposium Proceedings (AIMA 19,  November 16-20, Washington D.C., USA), pp.874-882 [PubMed].
• “Efficient Protocol for Collaborative Dictionary Learning in Decentralized Networks,”
  • Tsuyoshi Idé, Rudy Raymond, Dzung T. Phan,
  • In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 19,  August 10-16, Macao, China), pp.2585-2591 [slides, poster].
• “l₀–Regularized Sparsity for Probabilistic Mixture Models,”
  • Dzung T. Phan, Tsuyoshi Idé
  • In Proceedings of the SIAM International Conference on Data Mining (SDM 19,  May 2-4, Calgary, Alberta, Canada), pp.172-180 [slides].
• “Tensorial Change Analysis using Probabilistic Tensor Regression,”
  • Tsuyoshi Idé,
  • In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19,  January 27-February 1, Honolulu, Hawaii, USA), pp.3902-3909 [one pager, poster].
• “Collaborative Anomaly Detection on Blockchain from Noisy Sensor Data,”
  • Tsuyoshi Idé,
  • In Proceedings of 2018 IEEE International Conference on Data Mining Workshops (ICDMW); Workshop on Blockchain Systems for Decentralized Mining (BSDM, November 17, Singapore), pp.120-127 [slides, IEEE Xplore].
• “Multi-task Multi-modal Models for Collective Anomaly Detection,”
  • Tsuyoshi Idé, Dzung T. Phan, Jayant Kalagnanam,
  • Proceedings of the 2017 IEEE International Conference on Data Mining (ICDM 17, November 18-21, 2017, New Orleans, USA), pp.177-186 [slides].
• “A Novel l₀-constrained Gaussian Graphical Model for Anomaly Localization,”
  • Dzung T. Phan, Tsuyoshi Idé, Jayant Kalagnanam, Matt Menickelly, Katya Scheinberg,
  • Proceedings of the 17th International Conference on Data Mining Workshops (ICDMW 2017, November 18, 2017, New Orleans, USA), pp.830-833 [slides]
• “Sparse Gaussian Markov Random Field Mixtures for Anomaly Detection,”
  • Tsuyoshi Idé, Ankush Khandelwal, Jayant Kalagnanam,
  • Proceedings of the 2016 IEEE International Conference on Data Mining (ICDM 16, December 13-15, 2016, Barcelona, Spain), pp.955-960 [slides].
• “Unsupervised Object Counting without Object Recognition,”
  • Takayuki Katsuki, Tetsuro Morimura, Tsuyoshi Idé,
  • Proceedings of the 23rd International Conference on Pattern Recognition (ICPR 16, December 4-8, 2016, Cancun, Mexico), pp.3616-3621 [slides].
• “Change Detection using Directional Statistics (with corrections),”
  • Tsuyoshi Idé, Dzung T. Phan, Jayant Kalagnanam,
  • Proceedings of the Twenty-Fifrth International Joint Conference on Artificial Intelligence (IJCAI 16, July 9-15, 2016, New York, USA), pp.1613-1619 [slides].
• “Informative Prediction based on Ordinal Questionnaire Data,” [selected as a Best of ICDM]
  • Tsuyoshi Idé, Amit Dhurandhar,
  • Proceedings of the 2015 IEEE International Conference on Data Mining (ICDM 15, November 14-17, 2015, Atlantic City, USA), pp.191-200 [slides].
• “Latent Trait Analysis for Risk Management of Complex Information Technology Projects,”
  • Tsuyoshi Idé, Sinem Guven, Ee-Ea Jan, Sergey Makogon, Alejandro Venegas,
  • Proceedings of the 14th IFIP/IEEE International Symposium on Integrated Network Management (IM 2015, May 11-15, 2015, Ottawa, Canada), pp.305-312 [slides].
• “Probablistic Text Analytics Framework for information Technology Service Desk Tickets,”
  • Kuan-Yu Chen, Ee-Ea Jan, Tsuyoshi Idé,
  • Proceedings of the 14th IFIP/IEEE International Symposium on Integrated Network Management (IM 2015, May 11-15, 2015, Ottawa, Canada), pp.870-873.
• “A Probabilistic Concept Annotation for IT Service Desk Tickets,”
  • Ea-Ee Jan, Kuan-Yu Chen, Tsuyoshi Idé,
  • Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR 14, November 7, 2014, Shanghai, China), pp.21-23 [ACM Digital Library].
• “Probabilistic Two-Level Anomaly Detection for Correlated Systems,”
  • Bin Tong, Tetsuro Morimura, Einoshin Suzuki, Tsuyoshi Idé,
  • Proceedings of the 21st European Conference on Artificial Intelligence (ECAI 2014, August 18-22, 2014, Prague, Czech), pp.1109-1110 (supplemental material) [IOS Press].
• “Mining for Gold: How to Predict Service Contract Performance with Optimal Accuracy based on Ordinal Risk Assessment Data,”
  • Sinem Guven, Mathias Steiner, Tsuyoshi Idé, Sergey Makogon, Alejandro Venegas,
  • Proceedings of the 11th IEEE International Conference on Services Computing (IEEE SCC 2014, June 27-July 2, 2014, Anchorage, USA), pp.315-322 [slides].
• “Solving inverse problem of Markov chain with partial observations,”
  • Tetsuro Morimura, Takayuki Osogami, and Tsuyoshi Idé,
  • Proceedings of Neural Information and Processing Systems (NIPS 2013, December 5-10, 2013, Lake Tahoe, USA), pp.1655-1663.
• “Monitoring Entire-City Traffic using Low-Resolution Web Cameras,”
  • Tsuyoshi Idé, Takayuki Katsuki, Tetsuro Morimura, and Robert Morris,
  • Proceedings of  ITS World Congress Tokyo 2013 (October 14-18, 2013, Tokyo, Japan), #3143 [slides].
• “Identifying the optimal road closure with simulation,”
  • Takayuki Osogami, Hideyuki Mizuta, and Tsuyoshi Idé,
  • Proceedings of  ITS World Congress Tokyo 2013 (October 14-18, 2013, Tokyo, Japan), #3178.
• “Predicting Battery Life from Usage Trajectory Patterns,”
  • Toshiro Takahashi, Tsuyoshi Idé,
  • Proceedings of International Conference on Pattern Recognition (ICPR 2012, November 11-15, 2012, Tsukuba, Japan), pp.2946-2949.
• “X10-based massive parallel large-scale traffic flow simulation,”
  • Toyotaro Suzumura, Sei Kato, Takashi Imamichi, Mikio Takeuchi, Hiroki Kanezashi, Tsuyoshi Idé, Tamiya Onodera,
  • Proceedings of the ACM SIGPLAN 2012 X10 Workshop (X10 ’12, June 14, 2012, Beijing, China), Article No. 3, pp.3:1-3:4 [link].
• “Nonlinear Optimization to Generate Non-overlapping Random Dot Patterns,”
  • Takashi Imamichi, Hidetoshi Numata, Hideyuki Mizuta and Tsuyoshi Idé,
  • Proceedings of the Winter Simulation Conference 2011 (WSC 11, December 11-14, 2011, Phoenix, USA), pp.2419-2430 [slides].
• “Trajectory Regression on Road Networks,”
  • Tsuyoshi Idé and Masashi Sugiyama,
  • Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI 11, August 7-11, 2011, San Francisco, USA), pp.203-208 [slides].
• “Proximity-Based Anomaly Detection using Sparse Structure Learning,”
  • Tsuyoshi Idé, Aurelie C. Lozano, Naoki Abe, and Yan Liu,
  • Proceedings of 2009 SIAM International Conference on Data Mining (SDM 09, April 30-May 2, Sparks, USA), pp.97-108 [slides, datasets].
• “Travel-Time Prediction using Gaussian Process Regression: A Trajectory-Based Approach,”
  • Tsuyoshi Idé, and Sei Kato,
  • Proceedings of 2009 SIAM International Conference on Data Mining (SDM 09, April 30-May 2, Sparks, USA), pp.1185-1196 [slides].
• “Semi-Supervised Local Fisher Discriminant Analysis for Dimensionality Reduction,”
  • Masashi Sugiyama, Tsuyoshi Idé, Shinichi Nakajima, and Jun Sese,
  • Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2008, May 20-23, 2008, Osaka, Japan); Lecture Notes in Computer Science, Springer, Vol. 5012, 2008, pp.333-344 [link].
• “Unsupervised Change Analysis using Supervised Learning,”
  • Shohei Hido, Tsuyoshi Idé, Hisashi Kashima, Harunobu Kubo, and Hirofumi Matsuzawa,
  • Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2008, May 20-23, 2008, Osaka, Japan); Lecture Notes in Computer Science, Springer, Vol. 5012, 2008, pp.148-159 [link].
• “Computing Correlation Anomaly Scores using Stochastic Nearest Neighbors,”
  • Tsuyoshi Idé, Spiros Papadimitriou, and Michail Vlachos,
  • Proceedings of the Seventh IEEE International Conference on Data Mining (ICDM 07, October 28-31, 2007, Omaha, USA), pp.523-528 [slides].
• “Change-point detection using Krylov subspace learning,”
  • Tsuyoshi Idé and Koji Tsuda,
  • Proceedings of 2007 SIAM International Conference on Data Mining (SDM 07, April 26-28, Minneapolis, USA), pp.515-520 [slides].
• “Translational symmetry in subsequence time-series clustering,”
  • Tsuyoshi Idé,
  • New Frontiers in Artificial Intelligence: Proceeding of the 20th Annual Conferences of the Japanese Society for Artificial Intelligence (JSAI 2006, June 7-9, 2006, Tokyo), Lecture Notes in Artificial Intelligence, Springer, Vol. 4384, pp.5-18 [final version].
• “Why does Subsequence Time-Series Clustering Produce Sine Waves?,”
  • Tsuyoshi Idé,
  • Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 06, September 18-22, 2006, Berlin, Germany), Lecture Notes in Artificial Intelligence, Springer, Vol.4213, pp.311-322 [final version, slides].
• “Pairwise Symmetry Decomposition Method for Generalized Covariance Analysis,”
  • Tsuyoshi Idé,
  • Proceedings of the fifth IEEE International Conference in Data Mining (ICDM 05, November 20-27, 2005, Houston, USA), pp.657-660 [slides].
• “Knowledge Discovery from Heterogeneous Dynamic Systems using Change-Point Correlations,”
  • Tsuyoshi Idé and Keisuke Inoue,
  • Proceedings of 2005 SIAM International Conference on Data Mining (SDM 05, April 21-23, 2005, , Newport Beach, USA), pp.571-576 [slides].
• “Network-based Problem Detection for Distributed Systems,”
  • H. Kashima, T. Tsumura, T.Idé, T. Nogayama, R. Hirade, H. Etoh, and T. Fukuda,
  • Proceedings of the 21st International Conference on Data Engineering (ICDE 2005, April 5-8, 2005, Tokyo, Japan), pp.978-989.
• “Eigenspace-based Anomaly Detection in Computer Systems,”
  • Tsuyoshi Idé and Hisashi Kashima,
  • Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2004, August 22-25, Seattle, USA), pp.440-499 [slides].
• “Effective Dimension in Anomaly Detection: Its Application to Computer Systems,”
  • Tsuyoshi Idé and Hisashi Kashima,
  • Post-proceedings of the Eighteenth Annual Conference of Japanese Society of Artificial Intelligence (JSAI 2004, June 2-4, 2004, Kanazawa, Japan), Lecture Notes in Computer Science, Springer, Vol. 3609, pp.189-204 [final version].
• “Moire-Free Collimating Light Guide with Low-Discrepancy Dot Patterns,”
  • Tsuyoshi Idé, H. Numata, H. Mizuta, Y. Taira, M. Suzuki, M. Noguchi, and Y. Katsu,
  • Digest of Technical Papers (Society for Information Display 2002, May 19-24, 2002, Boston, USA), pp. 1232-1235 [slides].

Journal (refereed)

• “Sequential Uncertainty Quantification with Contextual Tensors for Social Targeting,”
  • Tsuyoshi Idé, Keerthiram Murugesan, Djallel Bouneffouf, Naoki Abe,
  • Knowledge and Information Systems, to appear, 2025 [link].
• “Identifying Primary Aldosteronism Patients who Require Adrenal Venous Sampling: A Multi-center Study,”
  • Takumi Kitamoto*, Tsuyoshi Idé*, Yuta Tezuka, Norio Wada, Yui Shibayama, Yuya Tsurutani, Tomoko Takiguchi, Kosuke Inoue, Sachiko Suematsu, Kei Omata, Yoshikiyo Ono, Ryo Morimoto, Yuto Yamazaki, Jun Saito, Hironobu Sasano, Fumitoshi Satoh, and Tetsuo Nishikawa (* equal contribution),
  • Scientific Reports, Vol. 13 (2023) 21722 [link, GitHub].
• “Diagnostic Spatio-temporal Transformer with Faithful Encoding,”
  • Jokin Labaien, Tsuyoshi Idé, Pin-Yu Chen, Ekhi Zugasti, Xabier De Carlos,
  • Knowledge-Based Systems, Vol. 274 (2023)110639 [link].
• “Signal and noise extraction from analog memory elements for neuromorphic computing,”
  • N. Gong, T. Idé, S. Kim, I. Boybat, A. Sebastian, V. Narayanan, T. Ando,
  • Nature Communications, Vol. 9, No. 2102 (2018).
• “City-Wide Traffic Flow Estimation from Limited Number of Low Quality Cameras,”
  • Tsuyoshi Idé, T. Katsuki, T. Morimura, and R. Morris,
  • IEEE Transactions on Intelligent Transportation Systems, Vol. 18, No. 4, pp.950-959 (2017) [link].
• “Supervised Item Response Models for Informative Prediction,”
  • Tsuyoshi Idé and Amit Dhurandhar,
  • Knowledge and Information Systems, Vol.51, No.1, pp.235–257, 2017 [link].
• “Toward simulating entire cities with behavioral models of traffic,”
  • T. Osogami, T. Imamichi, H. Mizuta, and Tsuyoshi Idé,
  • IBM Journal of Research and Development, Vol.57, Issue 5, pp.6:1-6:10, 2013 [link].
•  “Vehicle near-miss situation prediction from probe-car data using statistical machine learning,“
  • Tetsuro Morimura, Yusuke Tanizawa, Shinya yamasaki, Tsuyoshi Idé,
  • Transactions of Society of Automotive Engineers of Japan, Vol.43, No.2, pp.573-578, 2012.
• “Modeling Patent Quality: A System for Large-scale Patentability Analysis using Text Mining,”
  • Shohei Hido,Shoko Suzuki, Risa Nishiyama, Takashi Imamichi, Rikiya Takahashi, Tetsuya Nasukawa, Tsuyoshi Idé, Yusuke Kanehira, Rinju Yohda, Takeshi Ueno, Akira Tajima, and Toshiya Watanabe,
  • Journal of Information Processing, Vol. 20, No.3, pp.667-671 [link]
• “Trajectory Regression for Travel-Time Prediction,”
  • Tsuyoshi Idé, and Sei Kato,
  • Transactions of the Japanese Society for Artificial Intelligence, Vol. 25 (2010) , No. 3, pp.377-382 [link].
• “Unsupervised Change Analysis using Supervised Learning,”
  • Hirofumi Matsuzawa, Shohei Hido, Tsuyoshi Idé, and Hisashi Kashima,
  • IEICE Transactions on on Information and Systems, Vol.E93-D, No.6  pp.816-825, 2010 [link].
• “Semi-supervised local Fisher discriminant analysis for dimensionality reduction,”
  • Masashi Sugiyama, Tsuyoshi Idé, Shinichi Nakajima, and Jun Sese,
  • Machine Learning Journal, vol.78, no.1-2, pp.35-61, 2010 [link].
• “Recent Advances and Trends in Large-scale Kernel Methods,”
  • Hisashi Kashima, Tsuyoshi Idé, Tsuyoshi Kato, and Masashi Sugiyama,
  • IEICE Transactions on on Information and Systems, Vol.E92-D, No.7, pp.1338-1353, 2009 [link].
• “Network-Based Problem Detection for Distributed Systems,”
  • Hisashi Kashima, Tadashi Tsumura, Tsuyoshi Idé, Takahide Nogayama, Ryo Hirade, Hiroaki Etoh, and Takeshi Fukuda,
  • IEICE Transactions on Information and Systems, Vol. J89-D, No.2, pp.183-198, 2006 [link].
• “A novel dot-pattern generation to improve luminance uniformity of an LCD backlight,”
  • Tsuyoshi Idé, H. Numata, Y. Taira, H. Mizuta, M. Suzuki, M. Noguchi, and Y. Katsu,
  • Journal of the Society for Information Display, Vol.11, No.4 (2003) 659-665 [slides of the original paper].
• “Dot pattern generation technique using molecular dynamics,”
  • Tsuyoshi Idé, H. Mizuta, H. Numata, Y. Taira, M. Suzuki, M. Noguchi, and Y. Katsu,
  • Journal of the Optical Society of America, A, 20 (2003) 242-255 [link].
• “Nonlocal screening effect in Cu 4p(sigma) -1s resonant X-ray emission spectra of Nd2CuO4,”
  • Tsuyoshi Idé and Akio Kotani,
  • Journal of the Physical Society of Japan 69 (2000) 3107-3114 [link].
• “Interplay between Raman and fluorescence-like components in resonant X-ray emission spectra of degenerate d⁰and d¹ systems,”
  • Tsuyoshi Idé and Akio Kotani,
  • Journal of the Physical Society of Japan 69 (2000) 1895-1906 [link].
• “Polarization and momentum dependence of a charge-transfer excitation in Nd2CuO4,”
  • K. Hamalainen, J. P. Hill, S. Huotari, C. -C. Kao, L. E. Berman, A. Kotani, Tsuyoshi Idé, J. L. Peng, and R. L. Greene,
  • Physical Review B 61 (2000) 1836-1840 [link].
• “Local and nonlocal excitations in Cu 4p-1s resonant X-ray emission spectra of Nd2CuO4,”
  • Tsuyoshi Idé and Akio Kotani,
  • Journal of the Physical Society of Japan 68 (1999) 3100-3109 [link].
• “A model study on cluster size effects of resonant X-ray emission spectra,”
  • Tsuyoshi Idé and Akio Kotani,
  • Journal of the Physical Society of Japan 67 (1998) 3621-3629 [link].
• “Theoretical study on cluster size effects on X-ray absorption and resonant X-ray emission spectra in d and f electron systems,”
  • A. Kotani and Tsuyoshi Idé,
  • Journal of Synchrotron Radiation 6 (1999) 308-309, presented at XAFS X (International Union of Crystallography, Chicago, USA, 10-14 August 1998).

Book

Books

• Tsuyoshi Idé, Introduction to Anomaly Detection using Machine Learning ─ A Practical Guide with R, Corona Publishing, 2015 (in Japanese).
• Tsuyoshi Idé and Masashi Sugiyama, Anomaly Detection and Change Detection, Kodansha Scientific, 2015 (in Japanese).
• Kenji Yamanishi, Ryohei Hisano, Keishi Shimada, Tsubasa Minematsu, Tsuyoshi Idé, “From Anomaly Detection to Risk Management,” Saiensu-sha, 2022 (in Japanese).

Book chapters

• Tsuyoshi Idé, Making relationships simple — A story of graphical lasso, Iwanami Data Science, Vo.5 (2016) pp.48-63 [pdf] (in Japanese).
• Tsuyoshi Idé, 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 [pdf].
• Tsuyoshi Idé, Change detection from heterogeneous data sources. In Katsutoshi Yada, ed., Data Mining for Service. Springer Verlag, pp.221-243, 2014 [pdf].

Book editors

• Hisashi Kashima, Tsuyoshi Idé, Wen-Chih Peng (ed.), Advances in Knowledge Discovery and Data Mining (Proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023), Part I, II, III, IV.
• Hattori, T. Kawamura, Tsuyoshi Idé, 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 translations

• H. Fujisawa, Tsuyoshi Idé, Computer Age Statistical Inference. Kyoritsu, 2020 (Japanese translation of the book of the same title by Bradley Efron and Trevor Hastie, Cambridge University Press, 2016).
• M. Sugiyama, Tsuyoshi Idé, T. Kamishima, T. Kurita, and E. Maeda
  The Elements of Statistical Learning, Kyoritsu, 2014 (Japanese translation of the book of the same title by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie, Springer, 2001).
• Tsuyoshi Idé. Chap. 12, Continuous Latent Variables. In H. Motoda, T. Kurita, T. Higuchi, Y. Matsumoto, and N. Murata, ed., Pattern Recognition and Machine Learning. Maruzen, 2012 (Japanese translation of the book of the same title by Christopher Bishop, Springer, 2006).

Invited Talks

• “Explaining What Went Wrong: The Black-Box Anomaly Attribution Problem,”
  • IEOR Seminar, Department of Industrial Engineering and Operations Research, Columbia University (November 15, 2024) [slides].
• “Computing Input Responsibility Scores in Black-Box Anomaly Detection,”
  • Computer Science Department Colloquium, Rochester Institute of Technology (Oct. 8, 2024) [slides]
• “Decentralized Collaborative Machine Learning Framework with Democracy, Diversity, and Privacy,”
  • 2023 International workshop Blockchain Kaigi (BCK 23; Kobe, Japan, Oct. 2023) [slides]
• “Attributing anomalies from black-box predictions,”
  • Mini-symposium on Recent Advances in Computational Tools of Scientific Machine Learning towards Digital Twins, The 10th International Congress on Industrial and Applied Mathematics (August 22, 2023, Tokyo, Japan) [link, slides].
• “Explaining Anomalies of Black-Box Regression Function,”
  • Trustworthy AI Summit, Taipei Computer Association (January 13, 2023, Taipei, Taiwan) [slides].

• “Artificial Intelligence Seen from Behind the Scenes — A Personal View“,
  • Keynote, Research Exhibition 2022, Tokai Pathways to Global Excellence (November 2022, Nagoya University, Japan) [link].  
• “Basics and recent developments of anomaly detection: With a focus on causal discovery from event sequences (異常検知についての最近の話題: イベント系列からの因果発見問題を中心に),”
  • Open seminar, Division of Neural Dynamics, National Institute for Physiological Sciences, National Institutes of Natural Sciences (July 15, 2022) [link, slides]
• “Current landscape of artificial intelligence and a few recent topics (AIブームの現在と最近の研究から),”
  • Adachi Lab Seminar, Keio University (April 5, 2022), Tokyo (virtual), Japan. [blog article]
• “Machine learning now: A view from America (米国から見た機械学習の今), ”
  • Keynote, Special Session, The 50th SICE Symposium on Control Theory (March 9, 2022) [link, slides]
• “What’s going on in artificial intelligence research: A perspective from America (アメリカから見た人工知能研究の現在),”
  • Guest lecture, University of Tokyo (Course ID 4850-1026: Advanced Topics in Frontier Artificial Intelligence II, December 21, 2021) [link]
• “Anomaly detection and statistical machine learning: Foundations and recent advancements,”
  • SPS Seminar Series (University of the Western Cape, November 03, 2021) [slides].
• “Anomaly detection: Foundations and recent advancements,”
  • JAXA Seminar, Japan Aerospace Exploration Agency (October 22, 2021)
• “Decentralized Collaborative Learning with Probabilistic Data Protection,”
  • IEEE International Symposium on Blockchain (September 5-11, 2021) [link, slides].
• “Recent advances in machine learning from industrial sensor data,”
  • The 12th ICME International Conference on Complex Medical Engineering (CME 2018, September 6-8, 2018), Shimane,  Japan [link, slides].
• “Recent advances in sensor data analytics,”
  • Department Seminar, Department of Computer Science (January 10, 2018), University at Albany, State University of New York, Albany, USA  [slides].
• “Towards cognitive manufacturing,”
  • IEEE International Workshop on Data Mining for Service (DMS 2017, November 18, 2017), New Orleans, USA [link, slides].
• “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,”
  • Service Science Research Forum (May 10, 2012), Tokyo, Japan [follow-up book, slides].
• “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.

Other presentations

• “Applications of Graphical Gaussian Models for Process Control,”
  • Robert J. Baseman, Dzung T. Phan, Dhavalkumar C. Patel, Fateh A. Tipu, Jayant R. Kalagnanam, Tsuyoshi Idé,
  • In Proceedings of the Advanced Process Control Conference (APC 2019, October 28-31, 2019, Texas, USA).

Granted patents (as of May 2023)

1. Diagnosing Anomalies Detected By Black-box Machine Learning Models, October 31, 2022 – United States of America, 11487650
2. Learning Sparsity-constrained Gaussian Graphical Models In Anomaly Detection, January 3, 2022 – United States of America, 11216743
3. Incorporating Change Diagnosis Using Probabilistic Tensor Regression Model For Improving Processing Of Materials, August 24, 2020 – United States of America, 10754310
4. Managing Anomaly Detection Models For Fleets Of Industrial Equipment, August 3, 2020 – United States of America, 10733813
5. Diagnostic Fault Detection Using Multivariate Statistical Pattern Library, April 6, 2020 – United States of America, 10612999
6. Change Detection Using Directional Statistics, August 12, 2019 – United States of America, 10378997
7. Anomaly Detection Method, Program, And System, November 19, 2018 – United States of America, 10133703
8. Method For Cost Estimation Along Arbitrary Paths Using Weight Propagation, January 3, 2018 – Germany, 112010004005
9. Anomaly Detection Method, Program, And System, November 20, 2017 – United States of America,
   9824069
10. Anomaly Detection Method, Program, And System, October 30, 2017 – United States of America,
    9805002
11. System, Method, And Program For Predicting State Of Battery, March 1, 2017 – China, ZL201210392219.1
12. Anomaly Detection Method, Program, And System, November 14, 2016 – United States of America,
    9495330
13. %mirai-hc% Diagnosis Method For Medical Event Sequences, August 16, 2016 – China, ZL201280060060.1
14. Analysis Device, Analysis Method, And Program For Crash Simulation, August 11, 2016 – Japan,
    5984142
15. Information Processing Apparatus, Calculation Method, Program, And Storage Medium, May 30, 2016 – United States of America, 9354381
16. Information Processing Apparatus, Calculation Method, Program, And Storage Medium, May 2, 2016 – United States of America, 9329329
17. Calculating Risk Assessment Value Of Event Sequence, April 18, 2016 – United States of America,
    9317804
18. System, Method, And Program For Predicting State Of Battery, December 10, 2015 – Japan,
    5852399
19. %mirai-hc% Method, Apparatus And Computer Program For Calculating Risk Assessment Value Of Event Sequence, November 20, 2015 – Japan, 5839970
20. Information Processing Apparatus, Calculation Method, Program, And Storage Medium, September 3, 2015 – Japan, 5802041
21. Method For Cost Estimation Along Arbitrary Paths Using Weight Propagation, June 24, 2015 – China
    ZL201080057419.0
22. %mirai-hc% Diagnosis Method For Medical Event Sequences, February 12, 2015 – Japan, 5695763
23. Generation Of Uniformly Distributed Dot Patterns, December 17, 2014 – China, ZL201210107397.5
24. Method For Cost Estimation Along Arbitrary Paths Using Weight Propagation, November 21, 2014 – Japan, 5651129
25. Fault Detection Method For Trains, July 11, 2014 – Japan, 5576567
26. Kernel Regression System, Method, And Program, July 3, 2014 – Japan, 5570008
27. Cost Evaluation And Prediction, March 24, 2014 – United States of America, 8682633
28. Cost Evaluation And Prediction, December 3, 2013 – United States of America, 8600721
29. Kernel Regression System, Method, And Program, November 25, 2013 – United States of America, 8595155
30. Change Analysis, April 8, 2013 – United States of America, 8417648
31. Location Estimation System, Method And Program, March 25, 2013 – United States of America, 8405551
32. Location Estimation System, Method And Program, February 22, 2013 – Japan, 5203670
33. Travel Time Prediction Using Gaussian Process Regression, February 15, 2013 – Japan, 5198994
34. Proximity-based Anomaly Detection Using Sparse Structure Learning, January 25, 2013 – Japan, 5186322
35. Method For Cost Estimation Along Arbitrary Paths Using Weight Propagation, January 16, 2013 – United Kingdom, 2487701
36. Change Analysis System, Method And Program, December 20, 2012 – Japan, 5159368
37. Technique For Detecting Anomaly In Observation Target, March 23, 2012 – Japan, 4953239
38. Location Estimation System, Method And Program, March 20, 2012 – United States of America, 8138974
39. Online Outlier Detector For Directional Data, December 24, 2010 – Japan, 4652741
40. Method And System For Detecting Difference Between Plural Observed Results, December 7, 2010 – United States of America, 7849124
41. Diagnostic Data Detection And Control, May 18, 2010 – United States of America, 7720640
42. Pairwise Symmetry Decomposition Method For Generalized Covariance Analysis, April 20, 2010 – United States of America, 7702714
43. Anomaly Detection, January 11, 2010 – United States of America, 7647524
44. Scoring Method For Correlation Anomalies, May 4, 2009 – United States of America, 7529991
45. Computer Operation Analysis, February 17, 2009 – United States of America, 7493361
46. Methods Involving Computing Correlation Anomaly Scores, January 27, 2009 – United States of America, 7483934
47. Malfunction Condition Judgment Apparatus, Control Method, Automobile And Program, January 5, 2009 – United States of America, 7475052
48. System And Method For Detecting Difference Between Plural Observed Results, October 17, 2008 – Japan, 4201027
49. Method And Apparatus For Discovering Causal Relation From A Large Amount Of Various Time Series Data, September 12, 2008 – Japan, 4183185
50. Method And Apparatus For Filtering And Analyzing Signals From Automoblies, August 15, 2008 – Japan, 4170315
51. Anomaly Detection Based On Directional Data, July 29, 2008 – United States of America, 7406653
52. System And Method Of Evaluating Correlation, July 4, 2008 – Japan, 4148524
53. Anomaly Detection, March 18, 2008 – United States of America, 7346803
54. A Method Of Problem Determination For Computer Systems, March 14, 2008 – Japan, 4093483
55. Method And Apparatus For Anomaly Detection Of Multi-vertex Systems With Dynamic Correlation, March 2, 2007 – Japan, 3922375
56. Diagnostic Data Detection And Control, February 20, 2007 – United States of America, 7181365
57. Discrete Pattern, Apparatus, Method, And Program Storage Device For Generating And Implementing The Discrete Pattern, March 8, 2005 – United States of America, 6865325
58. Liquid Crystal Display Devices With Low-discrepancy Patterns And A Method For Generating Low-discrepancy Patterns, December 1, 2004 – Taiwan, I224698
59. Discrete Pattern, June 22, 2004 – United States of America, 6754419