▶What's New：
The book "Learning with the Minimum Description Length Principle" has been published from Springer.
See the web site of Yamanishi's laboratory for other information.
▼Profile
Kenji Yamanishi
Professor
Department of Mathematical Informatics
Graduate School of Information Science and Technology
The University of Tokyo.
Adress：731 Hongo, Bunkyoku, Tokyo, 1138656, JAPAN.
TEL: +81358416895
FAX: +81358418599
email: yamanishi att mist.i.utokyo.ac.jp
[Education]
 1992 Ph.D.

Mathematical Engineering, University of Tokyo, Japan.
Advisor: Prof. Shunichi Amari.
Dissertation: A Statistical Approach to Computational Learning Theory.
 1987 M.S.

Mathematical Engineering, University of Tokyo, Japan.
Advisor: Prof. Shunichi Amari.
Thesis: A Study on AlgebraicGeometric Codes. (in Japanese).
 1984 B.A.

Mathematical Engineering, University of Tokyo, Japan.
Advisor: Prof. Shunichi Amari.
Thesis: A Geometical Approach to Theories of Time Series and Systems.(in Japanese).
[Employment History]
 Apr. 2019―present

Professor, Department of Mathematical Informatics, Graduate School of
Information Science and Technology, The University of Tokyo.
 Apr. 2013―Mar. 2019

Professor, Department of Creative Informatics, Graduate School of
Information Science and Technology, The University of Tokyo.
 Jan. 2009― Mar. 2013

Professor, Department of Mathematical Informatics, Graduate School of
Information Science and Technology, The University of Tokyo.
 July 2002―Dec. 2008
 Research Fellow, NEC Corporation, Japan.
 July 2000―June 2002
 Principal Researcher, NEC Corporation, Japan.
 Sept.1995―June 2000
 Assistant Manager, NEC Corporation, Japan.
 Aug.1992ーAug.1995.
 Visiting Scientist, NEC Research Institute, Inc., NJ, U.S.A.
 July 1992.
 Assistant Manager, NEC Corporation, Japan.
 Apr.1987―June 1992.
 Researcher, NEC Corporation, Japan.
[Research Interest]
Theory: Informationtheoretic Learning TheoryComputational Learning Theory,
Statistical Inference, Computational Statistics, Theory of Latent Dynamics
Applications: Machine Learning, Data Mining (Anomaly Detection, Rule Induction),
Text Mining (Text Classification, Topic Analysis).
[Awards]
 2023.
 IEICE Fellow
 2015.
 The Best Teaching Award from the University of Tokyo
 2014.
 IBM Faculty Award
 2005.
 Advanced Technology Award Fuji Sankei Business Eye Award
 2004.
 Best Invention Award from NEC Corporation (Joint Work)
 2003.
 Contribution Award from NEC Corporation.
 2002.
 Contribution Award from NEC Corporation.
 1999.
 Contribution Award from NEC Corporation.
 1992.
 Contribution Award from NEC Corporation.
 1991.
 Uenohara Award from NEC Corporation.
 1991.
 Contribution Award from NEC Corporation.
 1990.

Best Paper Award from the Institute of Electronics, Information and Communication Engineers, Japan.
Paper:On New Asymptotic Performance Evaluation of Binary Modular Codes.
[Membership]
IEEE Information Theory Society, IEICE(Institute of Electronics, Information,
Communication, and Engineers), JSAI (Japanese Society of Artificial Intelligence),
SITA(Society of Information Theory and Its Applications)
[Link]
▼Publifications
[Refereed Journal Papers]
R.Yuki, Y.Ike, and K.Yamanishi: " Dimensionality selection for hyperbolic embedding using decomposed normalized maximum likelihood codelength," Knowledge and Information Systems, 2023, DOI: 10.1007/s10115023019342
K.Yamanishi and S. Hirai
"Detecting Signs of Model Change with Continuous Model Selection Based on Descriptive Dimensionality," Applied Intelligence, 2023.DOI : 10.1007/s10489023047805
S.Kyoya and K.Yamanishi: "Summarizing Finite Mixture Model with Overlapping Quantification," accepted for publication in Entropy, 2021.
Y. Hashimoto, T. Kiwaki, H. Sugiura, S. Asano, H. Murata, Y. Fujino, M. Matsuura, A. Miki, K. Mori, Y. Ikeda, T. Kanamoto, J. Yamagami, K. Inoue, M. Tanito, K. Yamanishi, R. Asaoka,: "Predicting 102 visual field from optical coherence tomography in glaucoma using deep learning corrected with 242/302 visual field" accepted for publication at Translational Vision Science and Technology.
K.Yamanishi, L.Xu, R.Yuki, S.Fukushima, and C. Lin: "Change Sign Detection with Differential MDL Change Statistics and Its Applications to COVID19 Pandemic Analysis" Scientific Reports, 11, Article number: 19795, 2021.
https://doi.org/10.1038/s41598021987814
R. Asaoka, L Xu, H. Murata, T. Kiwaki, M. Matsuura, Y. Fujino, M. Tanito, K. Mori, Y. Ikeda, T. Kanamoto, K. Inoue, J. Yamagami, and K. Yamanishi: "A joint multitask learning model for crosssectional and longitudinal predictions of visual field using optical coherence tomography" accepted for publication in Ophthalmology Science journal, 2021.
P.T. Hung and K.Yamanishi: " Word2vec Skipgram Dimensionality Selection via Sequential Normalized Maximum Likelihood", Entropy 2021, 23(8), 997;
https://doi.org/10.3390/e23080997
A.Suzukiand K.Yamanishi: "Fourieranalysisbased Form of Normalized Maximum Likelihood: Exact Formula and Relation to Complex Bayesian Prior", IEEE Transactions on Information Theory, Vol.67, 9, pp:61646178, 2021.
DOI:10.1109/TIT.2021.3088304
J.Huang, L.Xu, K.Qian, J.Wang, K.Yamanishi: "Multilabel learning with missing and completely unobserved labels", Data Mining and Knowledge Discovery, 35(3): pp:10611086, 2021.
https://doi.org/10.1007/s1061802100743x
L. Xu, R. Asaoka, H. Murata, T. Kiwaki, Y. Zheng, M. Matsuura, Y. Fujino, M. Tanito, K. Mori,Y. Ikeda, T. Kanamoto, K. Yamanishi: "Improving visual field trend analysis with optical coherence tomography and deeplyregularized latentspace linear regression",
Ophthalmology Glaucoma, Vol. 4, Issue 1, January–February Pages 7888, 2021.
DOI:doi.org/10.1016/j.ogla.2020.08.002
Y. Hashimoto, R. Asaoka, T. Kiwaki, H. Sugiura, S. Asano, H. Murata, M. Matsuura, A. Miki, K.Mori, Y. Ikeda, T.Kanamoto, J. Yamagami, K. Inoue, M. Tanito, K. Yamanishi: "Deep learning model to predict visual field in central 10 degrees from optical coherence tomography measurement in glaucoma" the British Journal of Ophthalmology, 105(4):bjophthalmol2019315600
DOI: 10.1136/bjophthalmol2019315600
L. Xu, R. Asaoka, T. Kiwaki, H. Murata, Y. Fujino, M. Matsuura, Y. Hashimoto,S. Asano, A. Miki, K. Mori, Y. Ikeda, T.Kanamoto, J. Yamagami, K. Inoue, M. Tanito, K. Yamanishi: "Predicting the glaucomatous central 10 degrees visual field from optical coherence tomography using deep learning and tensor regression," the American Journal of Ophthalmology, 2020.
DOI:doi.org/10.1016/j.ajo.2020.04.037
M.Okada, N.Masuda, K.Yamanishi: “Longtailed distributions of interevent times as mixtures of exponential distributions”, Royal Society Open Science, Published:26 February 2020,
DOI: doi.org/10.1098/rsos.191643
S. Fukushima, K. Yamanshi: "Detecting Metachanges in Data Streams from the Viewpoint of the MDL Principle", Entropy, 2019.
L. Xu, J. Wang, L. He, J. Cao, X. Wei, P. S. Yu, K. Yamanishi: “MixSp: A Framework for Embedding Heterogeneous Information Networks with Arbitrary Number of Node and Edge Types” accepted for publication for IEEE Transactions on Knowledge and Data Engineering, 2019
2019DOI: 10.1109/TKDE.2019.2955945.
T. Lee, S.Matsushima, and K.Yamanshi: "Grafting for combinatorial binary model using frequent itemset mining", Data Mining and Knowledge Discovery, 2019.
Y. Fu, S.Matsushima, K.Yamanishi:
"Model Selection for Nonnegative Tensor Factorization with Minimum Description Length", Entropy 2019, 21(7), 632.
DOI:10.3390/e21070632
K.Yamanishi, T.Wu, S.Sugawara, M.Okada:
"The decomposed normalized maximum likelihood codelength criterion for selecting hierarchical latent variable models", Data Mining and Knowledge Discovery, 2019.
DOI: 10.1007/s10618019006244
K. Moriya, S. Matsushima, K.Yamanishi:
“Traffic Risk Mining from Heterogeneous Road Statistics", IEEE Transactions on Intelligent Transportation Systems, vol 19(11), 36623675, 2018.
DOI:10.1109/TITS.2018.2856533, 2018
K. Miyaguchi and K. Yamanishi:: "Highdimensional Penalty Selection via Minimum Description Length Principle" Machine Learning Journal, 107(810), pp:12831302, 2018.
DOI:10.1007/s1099401857322
S. Yousefi, T. Kiwaki, Y. Zheng, H. Suigura, R. Asaoka, H. Murata, H. Lemij and K. Yamanishi: "Detection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning", American Journal of Ophthalmology,Vol. 193, pp: 7179, 2018.
DOI: 10.1016/j.ajo.2018.06.007.
K.Yamanishi and S.Fukushima::“Model change detection with MDL principle”, IEEE Transactions on Information Theory, 64(9), pp: 61156126, 2018.
DOI: 10.1109/TIT.2018.2852747.
K. Miyaguchi and K. Yamanishi: "Online detection of continuous chanages in stochastic processes,"
International Journal of Data Science and Analytics, 3:pp:213229, 2017.
DOI:10.1007/4106001700452
S. Sugawara, T. Wu, and K. Yamaishi:"
A basket twopart model to
analyze medical expenditure on interdependent multiple sectors",
Statistical Methods in Medical Research, Volume: 27 issue: 5, page(s): 15851600, Sep.2016.
DOI:
https://doi.org/10.1177/0962280216665642.
K.Morino, Y.Hirata, R.Tomioka, H.Kashima, K.Yamanishi, N.Hayashi, S.Egawa, and K.Aihara:
"Predicting disease progression from short biomarker series using expert advice algorithm"
Scientific Reports ,“ Vol. 5, 8953, 20th, May, 2015.,”
DOI: https://doi.org/10.1038/srep08953.
S.Saito, R.Tomioka, and K.Yamanishi:"Early Detection of Persistent Topics in Social Networks, Social Network Analysis and Mining.",pp:519,Dec.2015.
DOI: 10.1007/s1327801502571.
Y.Hayashi and K.Yamanishi“Sequential Network Change Detection with Its Applications to Ad Impact Relation Analysis,”
Data Mining and Knowledge Discovery: Volume 29, Issue 1 (2015), Page 137167,2015.
DOI:. 10.1007/s1061801303386.
S.Hirai, K.Yamanishi: “Efficient Computation of Normalized Maximum Likelihood Codes for Gaussian Mixtures with Its Applications to Clustering,”
IEEE Transaction on Information Theory 2013, vol.59, No.11, pp:77187727, 2013.
DOI: 10.1109/TIT.2013.2276036.
T.Takahashi, R.Tomioka, K.Yamanishi: “Discovering Emerging Topics in Social Streams via Link Anomaly Detection,”
IEEE Transactions on Knowledge and Data Engineering 2013, Vol.26,Issue1, pp:120130, Jan. 2014.
DOI: 10.1109/TKDE.2012.239.
R.Fujimaki, T.Nakata, H.Tsukahara, A.Sato, K.Yamanishi: “Mining Abnormal Patterns for Heterogeneous Time Series with Irrelevant Features for Fault Event Detection,”
Statistical Analysis and Data Mining, Vol.2, Issue 1, pp:117, 2009.
S.Hirose and K.Yamanishi: “Latent Variable Mining with Its Applications to Abnormal Behavior Detection,” Statistical Analysis and Data Mining, Vol.2, Issue 1, pp:7086, 2009.
K.Yamanishi and Y.Maruyama:“Dynamic Model Selection with Its Applications to Novelty Detection”,”
IEEE Transactions on Information Theory , pp：21802189, VOL 53, NO 6, June, 2007.
J.Takeuchi and K.Yamanishi: “A Unifying Framework for Detecting Outliers and Changepoints from Time Series”,”
IEEE Transactions on Knowledge and Data Engineering, 18:44, pp: 482492, 2006.
K.Yamanishi, J.Takeuchi, G.Williamas, and P.Milne: “Online Unsupervised Oultlier Detection Using Finite Mixtures with Discounting Learning Algorithms,”
Data Mining and Knowledge Discovery Journal, pp:275300, May 2004, Volume 8, Issue 3.
H.Li and K.Yamanishi:“Topic Analysis Using a Finite Mixture Model,”
Information Processing and Management,. Vol.39/4, pp 521541, 2003.
H.Li and K.Yamanishi:“Text Classification Using ESCbased Decision Lists,”
Information Processing and Management, .Vol. 38/3, pp 343361, March 2002.
K.Yamanishi: and H.Li:”Mining Open Answers in Questionare Data,”
IEEE Intelligent Systems. pp:5863、September/October, 2002.
K.Yamanishi: “Distributed Cooperative Bayesian Learning Strategies,”
Information and Computation, vol.150, p.2256, 1998.
K.Yamanishi: “A Decisiontheoretic Extension of Stochastic Complexity and Its Applications to Learning,”
IEEE Transactions on Information Theory, vol.44, 4, p.14241439, 1998.
K.Yamanishi: “Online Maximum Likelihood Prediction with respect to General Loss Functions,”
Journal on Computer and System Sciences, 55, p.105118, 1997.
H.Mamitsuka and K.Yamanishi: “alphaHelix Region Prediction with Stochastic Rule learning,”
CABIOS, vol.11, no.4, p.399411, 1995.
K.Yamanishi: “A Loss Bound Model for Online Stochastic Prediction Algorithms,”
Information and Computation, vol.119, 1, pp.3954, 1995.
K.Yamanishi: “Probably Almost Discriminative Learning,”
Machine Learning, vol.18, pp.2350, 1995.
K.Yamanishi: “Learning Nonparametric Densities in Terms of FiniteDimensional Parametric Hypotheses,”
IEICE Transactions, Inf.＆Syst., vol.E75D, no.4, July 1992.
K.Yamanishi: “A Learning Criterion for Stochastic Rules,”
Machine Learning, vol.9, pp.165203, 1992.
K.Yamanishi: “On Construction and Performance Evaluation of Fermat Codes,” (in Japanese)
IEICE Transactions, A, vol. J72A, no.3, p.597607, Mar. 1989.
K.Yamanishi: “On New Asymptotic Performance Evaluation of Binary Modular Codes,”(in Japanese)
IEICE Transactions, A, vol. J71A, no.12, p.21712181, Dec. 1988. (Best Paper Award from IEICIE)
K.Yamanishi: “On Derivation of Good Codes Based on Elliptic Codes and Hyperelliptic Codes,” (in Japanese)
IEICE Transactions, A, vol.J71A, no.10, p.19361946, Oct. 1988.
[Invited Journal Papers, Articles]
K. Yamanishi: “From Change Detection to Change Sign Detection” Mathematical Science, pp:2229, June, 2019.
K.Yamanishi: "Evolution of the Minimum Description Length Principle: From Basics to Recent Progress," Vol.10, No.3, pp:186194, 2017.
K.Yamanishi: "Discovery of Deep Knowledge from Complex Data、”Journal of the Japan Society of Mechanical Engineers, Vol.118, No.1163, pp:616619, Oct. 2015.
K.Yamanishi: "Anomaly detection: Outlier and change detection," Journal of Japan Reliability Society, May, 2015.
K.Yamanishi: Anomaly Detection with Data Mining, Kyoritsu Publishers, Inc. 2009
K.Yamanishi, S.Morinaga, N.Matsumura: “CGM Mining and Knowledge Organization,”
Information Processing, Vol.48 No.8、Sept.2007.
K.Yamanishi: “Applications of Data Mining to Information Security,”
Journal of Japanese Society of Artificial Intelligence、Vol.2１, No.5, pp:571576, 2006.
K.Yamanishi: “InformationBased Induction Sciences,”
to appear in Mathematical Science Encyclopedia, Maruzen.
K.Yamanishi and S.Morinaga: “Data Mining for Knowledge Organization,”
Journal of Advanced Technology、Vol.2, No.２, pp:129136, 2005.
K.Yamanishi, J.Takeuchi, Y.Maruyama: “Data Mining for Security, ”
Journal of Advanced Technology、Vol.2, No.1, pp:6369, 2005.
K.Yamanishi, J.Takeuchi, Y.Maruyama: “Three Types of Statistical Anomaly Detection, ”
Information Processing, vol.46, No.1, pp:3440, 2005
T.Egawa, M.Kobayashi, K.Yamanishi, A.Arutaki, J.Namiki: “Dynamic Collaboration from Scientists’ Eyes,”
Journal of Advanced Technology, pp:1726, vol.1, No.1, 2004.
K.Yamanishi, J.Takeuchi, Y.Matsunaga: “Security Mining”
NEC Technical Journal, Special Issue on Security, vol.56, No.12, pp:4145, NEC Corporation, 2003.
K.Yamanishi ：“Extended Stochastic Complexity and Its Applications to Learning”
in Advances in Minimum Description Length: Theory and Applications, The MIT Press
K.Yamanishi: “New Trend of Data and Text MiningOutlier Detection and Reputation Mining” (in Japanese)
Applied Mathematics. vol.12, No.4,p.722,2002.
S.Morinaga and K.Yamanishi: “Text Mining and Its Applications to Free Survey Data Analysis” (in Japanese)
Journal of the Society of Instrument and Control Engineers. Vol.41, No.5, pp:354357,2002.
K.Yamanishi: “Data and Text Mining,” (in Japanese)
in Iwanami: Statistical Science Frontier Series. Mar. 2003.
N.Abe, K.Yamanishi,A.Nakamura, H.Mamitsuka, J.Takeuchi, and H.Li : “Distributed and Active Learning,”
The Foundations of RealWorld Intelligence, Oct. 2001.
K.Yamanishi: “Data and Text Mining,” (in Japanese)
Journal of Computational Engineering, Oct. 2001.
.Yamanishi: “Current Status and Survey of InformationBased Induction Sciences,” (in Japanese)
Journal of Information Processing, vol.42, No.1, pp:915, 2001.
J.Takeuchi and K.Yamanishi: “Statistical Outlier Detection in Data Mining,” (in Japanese)
Applied Mathematics,2001
K.Yamanishi: “Statistical Model Selection and Machine Learning,” (in Japanese)
Journal of the Society of Instrument and Control Engineers, vol.38, p.420426, 1999.
K.Yamanishi: “Information Theory, Statistics, and Machine Learning,” (in Japanese)
Journal of the Society of Instrument and Control Engineers, vol.38, p.411412, 1999.
K.Yamanishi: “Extended Stochastic Complexity and Its Applications to Learning,” (in Japanese)
Applied Mathematics, vol.8, No.3, p.1429, 1998.
K.Yamanishi: “Minimum Description Length Principle,” (in Japanese)
Journal of Japan Society for Fuzzy Theory and Systems, vol.10, No.1, p.4350, 1998.
K.Yamanishi: “Data Compression and Learning,” (in Japanese)
Journal of Japanese Society for Artificial Intelligence, p.204215, vol.12 (2), 1997.
K.Yamanishi: “Stochastic Complexity and Learning Theory,” (in Japanese)
Operations Research, p.379386, vol.41 (7), 1996.
K.Yamanishi: “An Introduction to MDL: Computational Learningtheoretic View,” (in Japanese)
Journal of Japanese Society for Artificial Intelligence, p.435442, vol 7(3), May 1992.
K.Yamanishi and T.Han: “An Introduction to MDL: Informationtheoretic View,” (in Japanese)
Journal of Japanese Society for Artificial Intelligence, p.427434, vol 7(3), May 1992.
[Refereed Conference Papers]
S.Fukushima and K. Yamanishi: "Graph community augmentation with GMMmodeling in latent space", accepted for presentation at ICDM2024.
N. Nishikawa, Y. Ike, and K. Yamanishi: "Adaptive Topological Features via Persistent Homology Filetering Learning for Point Clouds”, Neurlips 2023.
Y. Li, L. Xu, and K. Yamanishi: "GMMDA: Gaussian Mixture Modeling of Graph in Latent Space for Graph Data Augmentation”, ICDM2023, pp:319328
S.Fukushima and K. Yamanishi: ""Balancing Summarization and Change Detection in Graph Streams", ICDM2023, pp:10251030.
R.Yuki, A.Suzuki, and K. Yamanishi: "Dimensionality and Curvature Selection of Graph Embedding using DNML CodeLength",ICDM2023, pp:15171522.
A.Suzuki, A.Nitanda, T.Suzuki, J.Wang, F.Tian, and K. Yamanishi:
Tight and Fast Generalization Error Bound of Graph Embedding in Metric Space, Proceedings of Internatonal Conference on Machine Learning (ICML2023), pp:3326833284, 2023.
R.Yuki, Y. Ike, and K. Yamanishi: ""Dimensionality Selection of Hyperboloc Graph Embedding using Decomposed Normalized Maximum Likelihood Codelength", Proceedings of ICDM2023, pp:666675, 2022.
K. Ueda, Y. Ike, and K. Yamanishi: "Change Detection with Probabilistic Models on Persistent Diagrams",Proceedings of ICDM2023, pp:11911196, 2022.
S. Hirai and K. Yamansihi: "Detecting Gradual Structure Changes of Nonparametric Distributions via Kernel Complexity", pp:1727, IEEE BigData 2021.
S. Fukushima, R. Kanai, and K. Yamansihi: "Graph Summarization with Latent Variable Probabilistic Models", pp:428440, Complex Networks 2021.
A.Suzuki, A.Nitanda, j. Wang, L. Xu, K. Yamanishi, and M. Cavazza: "Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic" pp:1243－1255 NeurIIPS 2021.
L.Xu L, R.Asaoka, T.Kiwaki, H.Murata, Y.Fujino, K.Yamanishi: PAMI: A Computational Module for Joint Estimation and Progression Prediction of Glaucoma. Proceedings of ACM International Conference on Knowledge Discovery and Data Mining (KDD2021), pp:38263834, August, 2021.
A.Suzuki, A.Nitanda, J.Wang, L.Xu, K. Yamanishi, and M.Cavazza: "Generalization Error Bound for Hyperbolic Ordinal Embedding" 2021 International Conference on Machine Learning (ICML2021), PMLR 139:1001110021, 2021.
S. Fukushima and K. Yamanishii: "Hierarchical change detection in latent variable models" 2020 IEEE International Conference on Data Mining(ICDM2020), pp:10281033, 2020.
DOI: 10.1109/ICDM50108.2020.00120
Jun Huang, Lincuan Xu, Jing Wang, Kenji Yamansihi: "Discovering Latent Class Labels for MultiLabel Learning", publication at IJCAIPRICAI 2020, pp:30583064, 2020.
DOI:10.24963/ijcai.2020/423
S. Hirai and K. Yamanishi: "Detecting model changes and their early signals using MDL change statistics", accepted for publication at 2019 IEEE International Conference on BigData (BigData2019).
A.Suzuki, A.Nitanda, j. Wang, L. Xu, K. Yamanishi, and M. Cavazza: "Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic" accepted for presentation at NeurIIPS 2021.
J. Vreeken and K.Yamanishi: "Modern MDL meets Data Mining Insights, Theory, and Practice," in Proceedings of 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining(KDD2019), 32293230, 2019.
Y.Zheng, L.Xu, T.Kiwaki, J.Wang, H.Murata, R.Asaoka, K.Yamanishi: "Glaucoma progression prediction using retinal thickness via latent space linear regression" Proceedings of 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining(KDD2019), pp:22782286, Aug. 2019
J.Wang , L.Xu , F.Tian , A.Suzuki C.Zhang , K.Yamanishi : “Attributed subspace clustering," Proceedings of International Joint Conference on Artificial Intelligence(IJCAI2019), pp:37193725, Aug. 2019
K. Miyaguchi and K. Yamanishi: " Adaptive minimax regret against smooth logarithmic losses over highdimensional l1balls via envelope complexity," Proceedings of AISTATS 2019, pp:34403448, Apr. 2019
J. Wang, A. Suzuki, L. Xu, F. Tian, L. Yang, K. Yamanishi："Orderly subspace clustering", Proceedings of AAAI 2019, pp:52645272, Jan. 2019.
S. Hirai and K.Yamanishi: “Detecting latent structure uncertainty with structural entropy”, Proceedings of IEEE International Conference on BigData (BigData2018), Dec. 2018. DOI: 10.1109/BigData.2018.8622283
H.Sugiura, T.Kiwaki, Y.Siamak, H.Murata, R.Asaoka,K.Yamanishi: “Estimating glaucomatous visual sensitivity from retinal thickness by using patternbased regularization and visualization”, Proceedings of 2018 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2018), pp: 783792, Aug.2018.
DOI: 10.1145/3219819.3219866.
J. Wang, Feng Tian ,W. Liu ,X. Wang, W. Zhang, K. Yamanishi:“Ranking preserving nonnegative matrix factorization,” Proceedings in International Joint Conference on Artificial Intelligence(IJCAI2018), pp:27762782, July, 2018.
DOI: https://doi.org/10.24963/ijcai.2018/385.
A. Suzuki, K. Yamanishi: “Exact calculation of normalized maximum likelihood code length using Fourier analysis,” International Symposium on Information Theory (ISIT2018), pp: 12111215, 2018.
R. Kaneko, K. Miyaguchi, and K. Yamanishi: "Detecting Changes in Streaming Data with InformationTheoretic Windowing,"
Proceedings of IEEE International Conference on Big Data (BigData2017),
pp:646655, Dec. 2017.
DOI: 10.1109/BigData.2017.8257980.
T. Kobayashi, S. Matsushima, L. Taito, and K. Yamanishi: "Discovering Potential Traffic Risk in Japan using Supervised Learning Approach,"
Proceedings of IEEE International Conference on Big Data (BigData2017), pp:948955,Dec. 2017.
DOI: 10.1109/BigData.2017.8258014.
T.Nakamura, T.Iwata, and K.Yamanishi: "Latent dimensionality estimation for probabilistic canonical correlation analysis using normalized maximum likelihood codelength,"
Proceedings of the 4th IEEE International Conference on Data Science and Advanced Analytics(DSAA2017), pp:716725, Oct. 2017.
DOI: 10.1109/DSAA.2017.39.
T. Wu, S. Sugawara, K. Yamanishi: "Decomposed Normalized Maximum Likelihood Codelength Criterion for Selecting Hierarchical Latent Variable Models,"
Proceedings of ACM International Conference on Knowledge Discovery and Data Mining (KDD2017), pp:11651174,
Aug. 2017.
DOI: 10.1145/3097983.3098110.
T.Uesaka, K.Morino, H.Sugiura, T.Kiwaki, H.Murata, R.Asaoka, K.Yamanishi: "Multiview Learning over Retinal Thickness and Visual Sensitivity on Glaucoma Eyes,"
Proceedings of ACM International Conference on Knowledge Discovery and Data Mining (KDD2017),pp:20412050,
Aug. 2017.
DOI: 10.1145/3097983.3098194.
K. Miyaguchi, S. Matsushima, K. Yamanishi:: "Sparse graphical modeling via stochastic complexity," Proceedings of 2017 SIAM International Conference on Data Mining (SDM2017), pp:723731, 2017.
DOI: 10.1137/1.9781611974973.81.
K.Yamanishi, K.Miyaguchi: "Detecting gradual changes from data streams using MDLchange statistics,"
Proceedings of 2016 IEEE International Conference on BigData (BigData2016),
pp:156163, Dec. 2016.
DOI: 10.1109/BigData.2016.7840601.
A.Suzuki, K.Miyaguchi, K.Yamanishi: "Structure selection for convolutive
nonnegative matrix factorization using normalized maximum likelihood
coding,"
Proceedings of 2016 IEEE International Conference on Data Mining (ICDM2016),
pp:12211226, 2016.
DOI: 10.1109/ICDM.2016.0163.
Y. Yonamoto, K. Morino and K. Yamanishi: ”Temporal network change
detection using network centrality," Proceedings of 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA 2016), pp:5160, Oct. 2016.
DOI: 10.1109/DSAA.2016.13.
T. Lee, S. Matsushima, and K. Yamanishi:
”Traffic risk mining using
partially ordered nonnegative matrix factorization,” Proceedings of 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA 2016), pp:622631, Oct. 2016.
DOI: 10.1109/DSAA.2016.71.
A. Demachi, S. Matsushima, and K.Yamanishi: "Web Behavior Analysis Using
Sparse NonNegative Matrix Factorization," Proceedings of 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA 2016), 574583, Oct. 2016.
DOI: 10.1109/DSAA.2016.85.
Y.Ito, S.Oeda, and K.Yamanishi: “Rank selection for nonnegative
matrixfactorization with normalized maximum likelihood coding." Proceedings of SIAM International Conference on Data Mining (SDM2016), pp:720728, Mar. 2016.
DOI: 10.1137/1.9781611974348.81.
K.Tomoda, K.Morino, R.Asaoka, H.Murata, and K.Yamansihi: “Predicting
glaucomatous progression with piecewise regression model from heterogeneous
medical data." Proceedings of 9th International Conference on Health Informatics (HEALTHINF2016), Feb.2016. (PDF: This contribution was presented at HEALTHINF2016: HEALTHINF web site), pp:93104, 2016.
DOI: 10.5220/0005703900930104.
K.Miyaguchi and K.Yamanishi: "Online detection of continuous changes in stochastic processes.," Proceedings of 2015 IEEE/ACM International Conference on Data Science and Advanced Analytics (DSAA’2015), Oct. 2015.
DOI: 10.1109/DSAA.2015.7344783.
K.Moriya S.Matsushima, and K.Yamansihi: "Traffic risk mining from heterogeneous road statistics.," Proceedings of 2015 IEEE/ACM International Conference on Data Science and Advanced Analytics (DSAA’2015), Oct. 2015.
DOI: 10.1109/DSAA.2015.7344889.
S. Maya, K. Morino, H. Murata, R. Asaoka, K. Yamanishi:"Discovery of glaucoma progressive patterns using hierarchical MDLbased clustering.," in
Proceedings of 2015 ACM International Conference on Knowledge Discovery and
Data Mining (KDD2015), pp:19791988, 2015.
DOI: 10.1145/2783258.2788574.
Y.Sakai and K.Yamanishi:Data fusion using restricted Boltzmann Machines.," Proceedings of IEEE International Conference on Data Mining(ICDM 2014):pp:953958, 2014.
DOI: 10.1109/ICDM.2014.70.
S.Maya, K.Morino, and K.Yamanishi:Predicting Glaucoma Progression using Multitask Learning with Heterogeneous Features.," Proceedings of IEEE BigData 2014: pp:261270, Oct. 2014.
DOI: 10.1109/BigData.2014.7004241.
S.Saito, R.Tomioka, K.Yamanishi: "Early Detection of Persistent Topics in Social Networks" in Proceedings of the IEEE/ACM International Conference on Social Networks Analysis and Mining (ASONAM2014), pp:417424, Aug.2014.
DOI: http://doi.ieeecomputersociety.org/10.1109/ASONAM.2014.6921620.
S.Oeda, Y.Ito, and K.Yamaishi: "Extracting Latent Skills from Time Series of Asynchronous and Incomplete Examinations.," Proceedings of the 7th International Conference on Educational Data Mining EDM2014,pp:367368, 2014.
S.Sato and K.Yamanishi:〝Graph partitioning change detection using treebased clustering,"Proceedings of IEEE International Conference on Data Mining(ICDM2013), 11691174, Dec.2013.
DOI: 10.1109/ICDM.2013.75.
Z. Liang, R. Tomioka, H. Murata, R. Asaoka, and K.Yamanishi:“Quantitative prediction of visual field loss due to glaucoma from few measurements,"Proceedings of IEEE International Conference on Data Mining(ICDM2013), pp:11211126, 2013.
DOI: 10.1109/ICDM.2013.93.
Y.Sakai and K.Yamanishi: “An NMLbased Model Selection Criterion for General Relational Data Modeling,”Proceedings of IEEE International Conference on Big Data (BigData 2013), pp:421429, Oct. 2013.
DOI: 10.1109/BigData.2013.6691603.
S.Oeda and K. Yamanishi:" Extracting Timeevolving Latent Skills from Examination Time Series."
In Proceedings of The Sixth International Conference on Educational Data Mining (EDM2013), Pp:340341, 2013.
Y. Hayashi and K. Yamanishi: Sequential network change detection with its applications to ad impact relation analysis.
In Proceedings of IEEE International Conference on Data Mining (ICDM2012), .pp.280289. 2012.
E.Sakurai and Kenji Yamanishi:Comparison of Dynamic Model Selection with Infinite HMM for Statistical Model Change Detection. In Proceedings of IEEE Information Theory Workshop 2012 (ITW2012),
pp:302306,Lausanne, Switzerland, Sept.2012.
H.Kanazawa and K.Yamanishi: An MDLbased ChangeDetection Algorithm with Its Applications to Learning Piecewise Stationary Memoryless Sources.
In Proceedings of IEEE Information Theory Workshop 2012 (ITW2012), pp:562566..2012.
S. Hirai and K. Yamanishi: Detecting Changes of Clustering Structures Using Normalized Maximum Likelihood Coding. In Proceedings of ACM Conference on Knowledge Discovery and Data Mining(KDD2012), pp:343351,ACM Press, Beijin China, Aug.,2013.
S. Hirai and K. Yamanishi: “Efficient computation of normalized maximum likelihood coding for Gaussian mixtures with its applications to optimal clustering”.
In Proceedings of IEEE International Symposium on Information Theory (ISIT2011), p.10311035, 2011.
T. Takahashi, R. Tomioka, and K. Yamanishi: “Discovering emerging topics in social streams via link anomaly detection.”
In Proceedings Of the IEEE International Conference on Data Mining (ICDM2011), pp.1230–1235, 2011.
Y. Urabe, K. Yamanishi, R. Tomioka, and H. Iwai: “Realtime changepoint detection using sequentially discounting normalized maximum likelihood coding.” The 15th PacificAsia Conference on Knowledge Discovery and Data Mining (PAKDD2011), 2011.
S.Hirose, K.Yamanishi, T.Nakata, R.Fujimaki:Network Anomaly Detection based on Eigen Equation Compression,”
in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2009), 2009.
R.Fujimaki, T.Nakata, H.Tsukahara, A.Sato, K.Yamanishi: “Mining Abnormal Patterns for Heterogeneous Time Series with
Irrelevant Features for Fault Event Detection,” in Proceedings of SIAM International Conference on Data Mining, 2008.
S.Hirose and K.Yamanishi: “Latent Variable Mining with Its Applications to Abnormal Behavior Detection,”
in Proceedings of SIAM International Conference on Data Mining, 2008.
S.Hirose and K.Yamanishi: “Latent Variable Mining with Its Applications to Abnormal Behavior Detection,” (in Japanese)
Proceedings of the 2007 Workshop on InformationBased Induction Sciences(IBIS2007), 2007.
N.Matsumura, S.Morinaga, and K.Yamanishi : “Mining Topic Structure Using Distributed Cooperative Learning,” (in Japanese)
Proceedings of the 2005 Workshop on InformationBased Induction Sciences(IBIS2005), 2005.
N.Matsumura, S.Morinaga, and K.Yamanishi : “Learning Global Topic Structure from Distributed Heterogeneous Sources,”
(in Japanese)Proceedings of the Fourth Forum on Information Technologies (FIT2005) , 2005.
K.Yamanishi and Y.Maruyama: “Dynamic Model Selection for Network Failure Monitoring,”
to appear in Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining (KDD2005), ACM Press, 2005.
Y.Maruyama and K.Yamanishi: “Dynamic Model Selection with Its Applications to Computer Security,”（in Japanese）
Proceedings of the 2004 Workshop on InformationBased Induction Sciences(IBIS2004), pp:1522, 2003.
Y.Maruyama and K.Yamanishi: “Dynamic Model Selection with Its Applications to Computer Security,”
Proceedings of 2004 IEEE International Workshop on Information Theory, 2004.
Y.Maruyama and K.Yamanishi: “Dynamic Model Selection with Its Applications to Computer Security,”
Proceedings of 2004 IEEE International Workshop on Information Theory, 2004.
S.Morinaga and K.Yamanishi: “Tracking Dynamics of Topic Trends Using a Finite Mixture Model,”(in Japanese)
Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2004),
ACM Press, 2004.
Y.Matsunaga and K.Yamanishi: “Dynamic Model Selection and Its Applications to Anomalous Behavior Detection,”(in Japanese)
Proceedings of the 2003 Workshop on InformationBased Induction Sciences(IBIS2003), 2003.
Y.Matsunaga and K.Yamanishi: “An Informationtheoretic Approach to Detecting Anomalous Behaviors,” (in Japanese)
Proceedings of the Second Forum on Information Technologies (FIT2003) , 2003.
S.Morinaga, K.Yamanishi, J.Takeuchi: “Distributed Cooperative Mining for Information Consortia,”
Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD2003),
ACM Press, 2003.
S.Morinaga, K.Yamanishi, J.Takeuchi: “Distributed Cooperative Mining from Different Information Sources,”(in Japanese)
Proceedings of the 2002 Workshop on InformationBased Induction Sciences(IBIS2002), 2002.
K.Yamanishi and J.Takeuchi: “A Unifying Approach to Detecting Outliers and ChangePoints from Nonstationary Time Series Data.”
Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD2002),
ACM Press, 2002.
S.Morinaga, K.Yamanishi, K.Tateishi, and T.Fukushima: “Mining Product Reputations on the Web.”
Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD2002),
ACM Press, 2002.
K.Yamanishi and J.Takeuchi: “A Unifying Approach to Detecting Outliers and ChangePoints from Nonstationary Data,”
Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD2002),
ACM Press, 2002.
H.Li and K.Yamanishi: “A Statistical Approach to Analgzing Open Answers in Quessionare Data ,” (in Japanese)
Proceedings of the 2001 Workshop on InformationBased Induction Sciences(IBIS2001), pp:129134, 2001.
K.Yamanishi and J.Takeuchi: “Discovering Outlier Filetering Rules From Unlabeled DataーCombininig Supervised Learners with
Unsupervised Learneresー.” Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and
Data Mining(KDD2001), ACM Press, pp:389394, 2001.
H.Li and K.Yamanishi: “Mining from Open Answers in Questionare Data.” Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD2001), ACM Press, pp:443449, 2001.
K.Yamanishi and J.Takeuchi: “Discovering Outlier Filetering Rules From Unlabeled DataCombininig Supervised Learners with
Unsupervised Learneres,” Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data
Mining(KDD2001), ACM Press, pp:389394, 2001.
H.Li and K.Yamanishi: “Statistical and Lexical Topic Analysis Using a Finite Mixture Model,”
Proceedings of ACL Workshop on Very Large Corpora, 2000.
H.Li and K.Yamanishi: “Statistical and Lexical Topic Analysis Using a Finite Mixture Model,”(in Japanese)
Proceedings of the 2000 Workshop on InformationBased Induction Sciences(IBIS2000), 2000.
K.Yamanishi and J.Takeuchi: “Statistical Outlier Detection Using Online Discounting Learning Algorithms,”(in Japanese)
Proceedings of the 2000 Workshop on InformationBased Induction Sciences(IBIS2000), 2000.
K.Yamanishi, J.Takeuchi, G.Williams, and P.Milne: “Online Unsupervised Oultlier Detection Using Finite Mixtures with Discounting
Learning Algorithms,” in Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
(KDD2000), ACM Press, pp:320324 2000.
H.Li and K.Yamanishi: “Text Classification Using ESCBased Decision Lists,”(in Japanese)
Proceedings of the 1999 Workshop on InformationBased Induction Sciences(IBIS99), pp.239244, 1999.
K.Yamanishi: “Extended Stochastic Complexity in Individual Sequence Analysis,”
Proceedings of the 1999 Workshop on InformationBased Induction Sciences(IBIS99), pp.163168, 1999.
H.Li and K.Yamanishi: “Text Classification Using ESCBased Decision Lists,”
Proceedings of International Conference on Information ＆ Knowledge Management(CIKM99), pp.122130, 1999.
K.Yamanishi: “Minimax Relative Sequence Analysis for Sequential Prediction Algorithms Using Parametric Hypotheses,”
Proceedings of the 11th Annual Conference on Computational Learning Theory(COLT98), pp.3243, 1998.
H.Li and K.Yamanishi: “Document Classification Using A Finite Mixture Model,”
Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics(ACL97), p.3947,
Morgan Kaufmann, 1997.
K.Yamanishi: “Distributed Cooperative Bayesian Learning Strategies,”
Proceedings of the Tenth Annual Conference on Computational Learning Theory(COLT97), pp.250262, ACM Press, 1997.
K.Yamanishi: “A Randomized Approximation of the MDL for Stochastic Models with Hidden Variables,”
Proceedings of the Eigth Annual Conference on Computational Learning Theory(COLT96), pp.99109, ACM Press, 1996.
K.Yamanishi: “Randomized Approximate Aggregating Strategies and Their Applications to Prediction and Discrimination,”
Proceedings of the Eigth Annual Conference on Computational Learning Theory(COLT95), pp.8390, 1995.
K.Yamanishi: “OnLine Maximum Likelihood Prediction with respect to General Loss Functions,”
Lecture Notes in Artificial Intelligence 904, Computational Learning Theory:
Second European Conference, EuroCOLT'95, pp.8498, Springer, 1995.
K.Yamanishi: “The Minimum Lcomplexity Algorithm and Its Applications to Learning Nonparametric Rules,”
Proceedings of the Seventh Annual ACM Workshop on Computational Learning Theory(COLT94), p.173182,
ACM Press, 1994.
K.Yamanishi: “OnLine Prediction Based on the Extended Stochastic Complexity,”
presented at Workshop on Descriptional Complexity,
organized by E.Pednault, Newbrunswick, NJ, 1994.
K.Yamanishi: “Learning Nonparametric Smooth Rules by Stochastic Rules with Finite Partitioning,”
Computational Learning Theory: EuroCOLT'93, pp.217228, Oxford, 1994.
K.Yamanishi: “On Polynomialtime Probably Almost Discriminative Learnability,”
Proceedings of the Sixth Annual ACM Conference on
Computational Learning Theory(COLT93), pp.94100, ACM Press, 1993.
H.Mamitsuka and K.Yamanishi: “Protein alphaHelix Region Prediction Based on StochasticRule Learning,”
Proceedings of the TwentySix Annual Hawaii International Conference on System Sciences(ICSS93), p.659668,
IEEE Computer Society Press, 1993.
H.Mamitsuka and K.Yamanishi: “Protein Secondary Structure Prediction Based on StochasticRule Learning,”
Proceedings of the Third Annual Workshop on Algorithmic Learning Theory(ALT92), pp.240251, 1993.
K.Yamanishi: “Probably Almost Discriminative Learning,”
Proceedings of the Fifth Annual ACM Workshop on Computational
Learning Theory(COLT92), pp.164171, ACM Press, 1992.
K.Yamanishi: “Learning Nonparametric Densities by Finite Dimensional Parametric Hypotheses,”
Proceedings of the Second Annual Workshop on Algorithmic Learning Theory(ALT92), pp.175186, JSAI Press, 1992.
A.Konagaya and K.Yamanishi: “Stochastic Decision Predicates: A New Scheme to Represent Motifs,” presented at AAAI
Workshop on AI and Molecular Biology, 1991.
A.Konagaya and K.Yamanishi: “Stochastic Decision Predicates: A New Scheme to Represent Motifs,”presented at AAAI
Workshop on AI and Molecular Biology, 1991.
K.Yamanishi and A.Konagaya: “Learning Stochastic Motifs from Genetic Sequences,”Proceedings of the Eighth International
Workshop on Machine Learning(ML91), pp.467471, Morgan Kaufmann, 1991.
K.Yamanishi: “A Learning Criterion for Stochastic Rules,”Proceedings of the Third Annual Workshop on Computational
Learning Theory(COLT90), pp.6781, Morgan Kaufmann, 1990.
K.Yamanishi: “Inferring Optimal Decision Lists from Stochastic Data Using the Minimum Description Length Criterion,”
presented at 1990 IEEE International Symposium on Information Theory(ISIT90), San Diego, CA, Jan. 1990.
K.Yamanishi: “On New Asymptotic Performance Evaluation of Binary Modular Codes,”presented at 1988 IEEE International
Symposium on Information Theory (ISIT88), Kobe Japan, June 1988.
[Invited Conference Papers]
K.Yamanishi: "Stochastic complexity for latent variable modeling,"
The 9th Workshop on Informationtheoretic Methods in Science and Engineering (WITMSE), Helsinki Finland, pp:4547, Sept.20, 2016.
K.Yamansihi: "Stochastic complexity for piecewise stationary memoryless
sources,"
presented at Sixth Workshop on InformationTheoretic Methods in Science
and Engineering (WITMSE 2013), Tokyo, JAPAN, 2013.
K.Yamanishi, E.Sakurai, and H.Kanazawa:
“Change Detection, Hyperthesis Testing, and Data Compression,”Proceedings
of Fifth Workshop on InformationTheoretic Methods in Science and Engineering
(WITMSE 2012), pp:2730, Amsterdam, Netherland, 2012
S.Hirai and K.Yamanishi:
"Clustering change detection using Normalized Maximum Likelihood Coding."
Proceedings of Fifth Workshop on InformationTheoretic Methods in Science and Engineering
(WITMSE 2012), pp:3132, 2012.
Extensions and Probabilistic Analysis of Dynamic Model Selection.”
Presented at 2010 Workshop on Information Theoretic Methods for Science and Engineering
(WITMSE 2010),Tampere, Finland, August 2010.
“Tracking Latent Dynamics” The first Workshop on Latent Dynamics, Tokyo, Japan, June, 2009.
“From Analytical Math to Strategic Math” Information Processing Conference,
Tokyo, Japan, March, 2009.
“Dynamic Model Selection with Its Applications to Data Mining”
presented at 2009 Workshop on Information Theoretic Methods for Science and Engineering
(WITMSE 2009),Tampere, Finland, August 2009.
K. Yamanishi:
“Dynamic and Heterogeneous Data Mining and Its Applications to CGM Media Analysis,”
presented at the first Symposium on Transdisciplinary Federation on Science and Technology,.
Dec.2006.
K.Yamanishi: “Cyber Crime Detection Based on Data Mining,”
Proceedings of Symposium on Information Security and Ai，
Nov. 2006.
K.Yamanishi: “CGM Media Security Mining and Knowledge Organization,”
Proceedings of Workshop on Information Value and
Knowledge Organization, Dec. 2006.
K.Yamanishi: “Text Mining and NLP Business,”(in Japanese)
Proceedings of 2003 JEITA Symposium on Natural Language
ProcessingNLP Business, 2003.
K.Yamanishi: “Text Mining,” (in Japanese)
Proceedings of the Second Forum on Information Technologies (FIT2003) , 2003.
K.Yamanishi: “Data Mining and Security,”(in Japanese)
Proceedings of the 17th Annual Conference on JSAI, Japan, June, 2002.
K.Yamanishi: “Data Mining Realizing Security/Web Intelligence,” (in Japanese)
Proceedings of AI Symposium, JSAI, pp:99104,
Japan, April, 2002.
K.Yamanishi: “Detecting Anomalies and Changepoints for Cyber Threat Analysis,”
Proceedings of IEEE Workshop on Data Mining
for Cyber Threat Analysis, Japan, December, 2002.
K.Yamanishi: “Web Mining and InformationBased Induction SciencesReputation Mining
and Log Mining, ”(in Japanese)
Proceedings of InformationBased Induction Sciences(IBIS2002), Japan, September, 2002.
K.Yamanishi and J.Takeuchi: “Anomaly Detection by Data Mining and Its Applications to
Network Intrusion Detection,” (in Japanese)
Proceedings of IEICE Information Network Research Group. Japan, June, 2002.
K.Yamanishi: “Web Mining and InformationBased Induction Sciences,” (in Japanese)
Proceedings of Information Science Symposium,
Japan, January 2002.
K.Yamanishi: “Text Mining Using Stochastic Modeling of Text Data,” (in Japanese)
Proceedings of Workshop on AI Symposium,
Tokyo, Japan, July 2001.
K.Yamanishi: “Data and Text Mining Based on InformationBased Induction Sciences,” (in Japanese)
Proceedings of Workshop on AI Fundamentals, Hokuriku, Japan, Mar. 2001.
K.Yamanishi and J.Takeuchi: “Data Mining and Business HPC,” (in Japanese)
Proceedings of NEC/HPC Workshop, Tokyo Japan,
Dec. 2000.
K.Yamanishi: “InformationBased Induction SciencesTrends and Related Topics,” (in Japanese)
Proceedings of Symposium of
the Institute on System, Control, and Information Engineers, pp:1724, May 2000.
K.Yamanishi:
“Extended Stochastic Complexity and Minimax Relative Loss Analysis,”
Algorithmic Learning Theory:The Tenth International Conference,
ALT'99, Proceedings, pp.2638, 1999.
K.Yamanishi:
“From MDL criterion to Extended Stochastic Complexity,” (in Japanese)
Proceedings of IEICE (Institute of Electronics, Information, Communication, and Engineers), 1998.
K.Yamanishi:
“A Decisiontheoretic Extension of Stochastic Complexity and Its Applications to Learning,”(in Japanese)
Proceedings of the 1998 Workshop on InformationBased Induction Sciences, pp.3341, 1998.
K.Yamanishi: “Generalized Stochastic Complexity and Its Applications to Learning,”
Proceedings of the 1994 Conference on Information Science and Systems, vol.2, pp.763768, 1994.
K.Yamanishi: “On Complexity of MDL Learning and Discrimination,”
Proceedings of 1993 IEEE Information Theory Workshop, p.3031, 1993.
K.Yamanishi: “A Statistical Approach to Computational Learning Theory,”
Proceedings of the Third NEC Research Symposium, pp.238276, SIAM, 1992.
K.Yamanishi: “Why does the MDL give an effective learning strategy?” (in Japanese)
Proceedings of the 5th Annual Conference for JSAI, p.7780, June 1991.
K.Yamanishi: “Computational Learning Theory and the MDL Principle,” (in Japanese)
Proceedings of Information Theory and Its Applications Workshop, p.5558, 1991.
K.Yamanishi: “On New Asymptotic Performance Evaluation of Binary Modular Codes,”
Proceedings of Workshop on Coding Theory, Osaka Japan, June 1988.
[Other Invited Talks]
“Deep Knowledge Discovery with Advanced Utilization of Latent Space," JSTRIKEN AIP Open Symposium, Dec.1st 2021.
“Sign Informatics," Japan AI Society, June. 10th 2021.
“Sign Informatics," Nagoya Univ. Sign Science Seminar, Jan. 13th 2021.
“Change detection, minimum description length, sign informatics," JST Mathematics Seminar, Dec. 9th, 2020.
“Information Technologies for Realizing Social Activities with Social Distancing," Online symposium "Proposal toward a new information society in the post corona world", Jul. 4th, 2020.
“Computational OphthalmologyAn AI/Data Science Approach to Ophthalmology" TRF Workshop, Oct.23rd, 2019.
“Anomaly Sign Detection with Data Mining and Its Applications to Medicine” The Japan Brain Dock Society June 21st, 2019.
“Data Science Exploring Latent Knowledge” IEICE Technical Committee on Service Computing, May 31st, 2019.
“Case Studies of Applications of AI and Data Science to Ophthalmology,” Japanese Ophthalmological Society, Ophthalmological AI and BigData Committee. April 21st, 2019.
“Anomaly Detection with Data Mining,” Canon Institute for Global Studies, Dec.4th, 2018.
“MDLbased Gradual Model Change Detection,” Workshop on InformationTheoretic Methods for Science and Engineering (WITMSE 2017), September 13th, 2017.
"Discovering deep knowledge from heterogenous data,” ACM2017 International Conference on Data Mining, Communications and Information Technology(DMCIT 2017), May 26th, 2017.
"Change Symptom Detection and Information Security,”Institute of Information Security, ISS Square Workshop, Jan.27th, 2017.
"Anomaly Detection with Data Mining,”Keynote Speech at workshop in Nippon Steel and Sumitomo Metal Corporation, Dec.19th, 2016.
"Evolution of Minimum Description Length Principle," At Hiroshima City University Seminar, Oct.7th, 2016.
"Discovery of Latent Knowledge from Complex Data," JSTNSF International Symposium, May 12th, 2016.
“Evolution of the MDL," IEICE Symposium, Mar. 2016.
“Discoveery of Deep Knowledge from Complex Data," Symposim on An approach to AI from Information Science and Engineering, Mar. 2016.
“Anomaly Detection Technologies for Information Technologies," Bank of Japan Symposium on Information Security, Mar. 2016.
“Anomaly detection with data mining,” The University of Tokyo Symposium,
Japan Information Processing Society Coference, 2015.
“Discovery of Deep Knowledge from Complex Data,”
Toward Social Design in 2020, Tokyo, June, 2015.
“Discovering deep knowledge from complex big data,”
IEE Workshop on Nanotechnologies, July 2014.
“Discovering deep knowledge from latent space,”
Applied Statistics Society Symposium, May 2014.
“Discovering deep knowledge from latent space,”
Workshop on Cryptography Frontiers,March, 2014.
K.Yamanishi, E.Sakurai, and H.Kanazawa:
“Change Detection, Hypothesis Testing, and Data Compression.”
Proceedings of Fifth Workshop on InformationTheoretic Methods in Science and Engineering
(WITMSE 2012),pp:2730, 2012.
S.Hirai and K.Yamanishi:
“Clustering change detection using Normalized Maximum Likelihood Coding.”
Proceedings of Fifth Workshop on InformationTheoretic Methods in Science and Engineering
(WITMSE 2012),pp:3132, 2012.
“Detecting Changes of Latent Structures from Network Type Series” Institute of Statistical Mathematics.
Workshop on Mathematics/Mathematical Science and Its Collaboration with Sciences and Industries:
Machine Learning Approaches to Network Type Knowledge, Feb.2012.
“Detecting Changes of Latent Structures from Time Series”
IEICE Pattern Recognition “Media Understanding Research Society,Feb.2012.
“Recent advances in informationtheoretic learning theory―Latent Dynamics―
IEICE Information Theory Research Society, Nov.2011
“Dynamic Model Selection with Its Applications to Data Mining”
presented at a seminar at IBM Japan, July 2009.
“Dynamic Model Selection with Its Applications to Data Mining”
presented at a seminar at CWI, July 2009
“Latent Dynamics Mining”
presented at Workshop on Math for Industries at Univ. of Kyushu, June 2009
“Applications of Data Mining to Information Security,”
presented at Chuo Univ. Open Seminar, July 2007.
“Security Mining for Safe Information Utilization,”
presented at OSPG Seminar, Mar.2006.
“Research and Development on Data Mining for Industrial Companies,”
presented at Kyoto University (hosted by Prof.Yamamoto), Dec.2005.
“Data MiningToward Security Intelligence and Knowledge Organization,”
presented at Tsukuba University (hosted by Prof.Tsubaki), July 2004.
“Web Mining,”
presented at JEITA Research Seminar, June 2003.
“Latest Data Mining Technologies with Their Applications to CRM,”
presented at Datawarehouse and CRM Expo. Tutorial Seminar, Tokyo Japan, June 2001.
“Online Unsupervised Outlier Detection Using Finite Mixture Models,”
presented at Toward a New Unification of Statistical Sciences, Neural Networks, and Data Mining,
The Institute of Statist. Math., Tokyo Japan, Nov. 2000.
“Information MiningFraud Detection and Text Mining,”
presented at Statistical Sciences and Data Mining, The Institute of Statist. Math., Tokyo Japan, October 1999.
“Extended Stochastic Complexity and Machine Learning,”
presented for Seminar at University of Tokyo (hosted by Prof.Yamamoto), Tokyo Japan, May 1999.
“Extended Stochastic Complexity and Learning Theory,”
presented for Seminar at ElectroCommunication University (hosted by Prof. Tesun Han),
Tokyo Japan, December 1998.
“Extended Stochastic Complexity and Learning Theory,”
presented for Seminar at Waseda University (hosted by Prof.Matsushima), Tokyo Japan, May 1998.
“InformatinBased Induction Sciences,”
presented at Workshop on Mathematical Engineering Methods for Statistical Information Processing,
The Institute of Statist. Math., January 1998.
“A Decisiontheoretic Extension of Stochastic Complexity and Its Applications to Learning,”
presented for Seminar at University of Tokyo (hosted by Prof.Tsujii), Tokyo Japan, October 1997.
“A Decisiontheoretic Extension of Stochastic Complexity and Its Applications to Learning,”
presented for Seminar at University of Tokyo (hosted by Prof.K.Hayami), Tokyo Japan, Feb. 1996.
“The Minimum LComplexity Algorithm and Its Applications to Learning,”
presented for Seminar at AT＆T Bell Laboratories, Murray Hills (hosted by Y.Freund), NJ U.S.A., Feb. 1994.
“Universal Discrimination Using the MDL Principle,”
presented for Seminar at ElectroCommunication University (hosted by Prof.H.Morita), Tokyo Japan, July 1992.
“Learning Theory and the MDL Principle,”
presented at Workshop on Pattern Recognition, University of Tokyo, Tokyo Japan, Feb. 1992.
“Learning Based on the MDL Principle,”
presented for Seminar at IBM Almaden Research Center (hosted by J.Rissanen), CA, U.S.A., July 1991.
“A Theory of Learning Stochastic Rules,”
presented for Seminar at University of Tokyo (hosted by Prof.S.Amari), Tokyo Japan, July 1991.
“AlgebraicGeometric Codes,”
presented for Seminar at ElectroCommunication University (hosted by Prof.H.Mizuno), Tokyo Japan, Nov. 1987.
“AlgebraicGeometric Codes,”
presented at Workshop on Combinatorial Theory and Its Applications, Tsukuba Japan, July 1987.
“AlgebraicGeometric Codes,”
presented for Seminar at Yokohama National University (hosted by Prof.H.Imai), Kanagawa Japan, Feb. 1987.
Note: The list here does not include any papers published without being reviewed, except invited papers.
Please contact me directly if you wish to look at them.
[Professional Activites]
Lecturer
A Special Lecture at Graduate School at University of Tokyo from Nov.2000 to Feb.2001.
[Comimittees]
Senior program committee member on KDD2014
Program chairs (jointly with Junichi Takeuchi) of WITMSE 2013
JST Sakigake「BigData Foundation」Domain Adviser 2013～
Senior program committee member on KDD2013
Honorary Chairs on WITMSE2012
JST CREST (Creation and Integration of Theory and Foundations for
Distributed Cooperative Energy Management Systems) Domain Adviser
Senior program committee member on ACML2010
Technical Cochair on WITMSE2010
Program committee member on ALT2010
Program committee member on KDD2010
Chair on Latent Dynamics Workshop 2010
Chair on IEICE InformationBased Induction Sciences and Machine Learning Society, 2010—
Program committee member on KDD2007
rogram committee member on KDD2006
Steering committee member on Society on Information Theory and Its Applications, JAPAN
Program committee member on KDD2005
Program committee member on ALT2005
Program committee member on IJCAI2005.
Chair on 2004 IBIS'04 (InformationBased Induction Sciences) , 2004.
Program committee member on KDD2004 (ACM Conference on Knowledge Discovery and Data Mining).
CoChair on IJCNLP2004 (First International Conference on Natural Language Processing), 2004.
Member on Editorial Boad on Program on Special Issue of InformationBased Induction Sciences in IEICE
(Institute of Electronics, Information, Communication, and Engineers), 2004.
Program committee member on OTC03 (3rd Workshop on Operational Text Classification), 2003
FIT(Forum on Information Technologies) Program committee member on FIT 2002.
Program committee member on DS'02 (Conference on Discovery Science), 2002.
Program committee member on IBIS 2002 (InformationBased Induction Sciences), 2002.
Member on Editorial Boad on Program on Special Issue of InformationBased Induction Sciences in IEICE
(Institute of Electronics, Information, Communication, and Engineers), 2002.
Member on Society Editorial Boad, Information Systems in IEICE
(Institute of Electronics, Information, Communication, and Engineers), 2001.
Member on Editorial Boad, Fundamentals in IEICE
(Institute of Electronics, Information, Communication, and Engineers), 2001
Member on Editorial Boad on Special Issue of InformationBased Induction Sciences in IEICE
(Institute of Electronics, Information, Communication, and Engineers), 2001.
Program committee member on IBIS 2001 (InformationBased Induction Sciences), 2001.
Chair of TimeLimited Research Committee on InformationBased Induction Sciences, IEICE
(Institute of Electronics, Information, Communication, and Engineers), Information Systems Society, 20012003.
Program committee member on Special Issue of InformationBased Induction Sciences
in Journal of Japanese Society of Artificial Intelligence, 2000
Program committee member on IBIS 2000 (InformationBased Induction Sciences), 2000.
Editor of Special Issue of InformationBased Induction Sciences in IEICE
(Institute of Electronics, Information, Communication, and Engineers), 1999.
Program chair on IBIS'99 (InformationBased Induction Sciences), 1999.
Editor of Special Issue of Information Theory, Statistical Methods, and Machine Learning in SICE
(Society of Instrument and Control Engineers), 1999.
Program committee member on COLT'99 (ACM Conference on Computational Learning Theory), 1999.
Committee member on Information Theory Society of IEICE
(Institute of Electronics, Information, Communication, and Engineers).
Chair of 1998 Workshop on IBIS'98 (InformationBased Induction Sciences), 1998.
Advisory committee member on COLT'97 (International Conderence on Computational Learning Theory), 1997.
Program committee member on ALT'96 (Workshop on Algorithmic Learning Theory), 1996.
Program committee member on WCNN'95 (World Conference on Neural Networks), 1995.
Program committee member on ML'95 (International Conference on Machine Learning), 1995.
Program committee member on EuroCOLT'95 (European Conference on Computational Learning Theory), 1995.
Program committee member on ML'94 (International Conference on Machine Learning), 1994.
Member of COLT (Computational Learning Theory) Working Group since 1994.
Program committee member on COLT'93 (ACM Conference on Computational Learning Theory), 1993.
Referee for Journal Submission
IEEE Transactions on Information Theory.
IEEE Transactions on Neural Networks.
Journal of Computer and System Sciences.
Information and Computation.
SIAM Journal on Computing.
Machine Learning.
Theoretical Computer Science.
Information Processing Letters.
IEICE (The Institute of Electronics, Information and Communication Engineers) Transactions.
Journal of Japan Society for Fuzzy Theory and Systems
Journal of Japan Society for Artificial Intelligence
[Tenure／Dissertation Committees]
Petri Kontkanen  received PhD with the paper; "Computationally Efficient Methods for MDLOptimal Density Estimation and Data Clustering," from Helsinki University.
Peter GrunwaldReceived Ph.D with the paper "The Minimum Description Length Principle and Reasoning Under Uncertainty
"from Universiteit van Amsterdam, 1999.
Vijay RagavanPromoted to Associate Professor with tenure in Vanderbilt University, 1995.