Japanese
English
1. ジャーナル・論文誌
Y. Li, L. Xu, and K. Yamanishi "GMMDA: Gaussian Mixture Modeling of Graph in Latent Space for Graph Data Augmentation” Knowledge and Information Systems, 2024.
R.Yuki, Y.Ike, and K.Yamanishi: " Dimensionality selection for hyperbolic embedding using decomposed normalized maximum likelihood code-length," Knowledge and Information Systems, 2023, DOI: 10.1007/s10115-023-01934-2
K.Yamanishi and S. Hirai "Detecting Signs of Model Change with Continuous Model Selection Based on Descriptive Dimensionality," Applied Intelligence, 2023.DOI : 10.1007/s10489-023-04780-5
C.Lin, L.Xu, and K.Yamanishi: "Network Change Detection based on Random Walk in Latent Space" IEEE Transactions on Knowledge and Data Engineering,Volume: 35, Issue: 6, 01 pp: 6136 - 6147, June 2023. DOI: 10.1109/TKDE.2022.3167062
S.Kyoya and K.Yamanishi: "Mixture Complexity and Its Application to Gradual Clustering Change Detection" Entropy, 2022. https://doi.org/10.3390/e24101407
S.Kyoya and K.Yamanishi: "Summarizing Finite Mixture Model with Overlapping Quantification" 23(11), 1503, Entropy, 2021. https://doi.org/10.3390/e23111503
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 10-2 visual field from optical coherence tomography in glaucoma using deep learning corrected with 24-2/30-2 visual field" publication at Translational Vision Science and Technology, November 2021, Vol.10, 28. doi:https://doi.org/10.1167/tvst.10.13.28.
K.Yamanishi, L.Xu, R.Yuki, S.Fukushima, and C. Lin: "Change Sign Detection with Differential MDL Change Statistics and Its Applications to COVID-19 Pandemic Analysis" Scientific Reports, 11, Article number: 19795, 2021. https://doi.org/10.1038/s41598-021-98781-4
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 cross-sectional and longitudinal predictions of visual field using optical coherence tomography" Ophthalmology Science, Volume 1, Issue 4, December,2021. https://doi.org/10.1016/j.xops.2021.100055
P.T. Hung and K.Yamanishi: " Word2vec Skip-gram Dimensionality Selection via Sequential Normalized Maximum Likelihood", Entropy 2021, 23(8), 997;
https://doi.org/10.3390/e23080997
https://doi.org/10.1167/tvst.10.13.28
A.Suzuki and K.Yamanishi: "Fourier-analysis-based Form of Normalized Maximum Likelihood: Exact Formula and Relation to Complex Bayesian Prior", IEEE Transactions on Information Theory, Vol.67, 9, pp:6164--6178, 2021.
DOI:10.1109/TIT.2021.3088304
J.Huang, L.Xu, K.Qian, J.Wang, K.Yamanishi: "Multi-label learning with missing and completely unobserved labels", Data Mining and Knowledge Discovery, 35(3): pp:1061-1086, 2021.
https://doi.org/10.1007/s10618-021-00743-x
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 deeply-regularized latent-space linear regression",
Ophthalmology Glaucoma, Vol. 4, Issue 1, January–February Pages 78-88, 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):bjophthalmol-2019-315600 DOI: 10.1136/bjophthalmol-2019-315600
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: “Long-tailed distributions of inter-event times as mixtures of exponential distributions”, Royal Society Open Science, Published:26 February 2020,
DOI: doi.org/10.1098/rsos.191643
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” IEEE Transactions on Knowledge and Data Engineering, 2019
DOI: 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, 34(1), pp:101-123, 2020.
DOI: doi.org/10.1007/s10618-019-00657-9
S. Fukushima, K. Yamanshi: "Detecting Metachanges in Data Streams from the
Viewpoint of the MDL Principle", Entropy,21(7)1134, 2019.
doi.org/10.3390/e21121134
KY. Fu, S.Matsushima, K.Yamanishi: "Model Selection for Non-negative 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 code-length criterion for selecting hierarchical latent variable models", Data Mining and Knowledge Discovery,33(4): 1017-1058, 2019.
DOI: 10.1007/s10618-019-00624-4
K. Moriya, S. Matsushima, K.Yamanishi:
“Traffic Risk Mining from Heterogeneous Road Statistics", IEEE Transactions on Intelligent Transportation Systems, vol 19(11), 3662-3675, 2018.
DOI:10.1109/TITS.2018.2856533, 2018
K. Miyaguchi and K. Yamanishi: "High-dimensional Penalty Selection via Minimum Description Length Principle" Machine Learning Journal, 107(8-10), pp:1283-1302, 2018.
DOI:10.1007/s10994-018-5732-2
S. Yousefi, T. Kiwaki, Y. Zheng, H. Sugiura, 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: 71-79, 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: 6115-6126, 2018.
DOI: 10.1109/TIT.2018.2852747.
K. Miyaguchi and K. Yamanishi:,"
"On-line detection of continuous chanages in stochastic processesInternational Journal of Data Science and Analytics", 3:pp:213-229, 2017.
DOI:10.1007/41060-017-0045-2
S. Sugawara, T. Wu, and K. Yamaishi: "A basket two-part model to
analyze medical expenditure on interdependent multiple sectors," Statistical
Methods in Medical Research, Volume: 27 issue: 5, page(s): 1585-1600, 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:5-19,Dec.2015.
DOI: 10.1007/s13278-015-0257-1.
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 137-167,2015.
DOI:. 10.1007/s10618-013-0338-6.
S.Hirai and K.Yamanishi: "Efficient Computation of Normalized Maximum Likelihood Codes for Gaussian Mixtures with Its Applications to Clustering," IEEE Transaction on Information Theory. vol.59, No.11, pp:7718--7727, 2013.
DOI:10.1109/ISIT.2011.6033686
T.Takahashi, R.Tomioka, K.Yamanishi: "Discovering Emerging Topics in Social Streams via Link Anomaly Detection,"IEEE Transactions on Knowledge and Data Engineeing, Vol.26,Issue1, pp:120--130, Jan. 2014.
DOI: 10.1109/TKDE.2012.239.
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:70-86, 2009.
R.Fujimaki, T.Nakata, H.Tsukahara, A.Sato and 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:1-17, 2009.
K.Yamanishi and Y.Maruyama: "Dynamic Model Selection with Its Applications to Novelty Detection, "
IEEE Transactions on Information Theory, pp:2180-2189, VOL 53, NO 6, June, 2007.
J.Takeuchi and K.Yamanishi: "A Unifying Framework for Detecting Outliers and Change-points from Time Series,"
IEEE Transactions on Knowledge and Data Engineering, 18:44, pp:482-492, 2006.
K.Yamanishi, J.Takeuchi, G.Williamas, and P.Milne: "On-line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms," Data Mining and Knowledge Discovery Journal, pp:275-300, 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:521-541, 2003.
H.Li and K.Yamanishi: "Text Classification Using ESC-based Decision Lists,"
Information Processing and Management, Vol. 38/3, pp:343-361, March 2002.
K.Yamanishi: and H.Li: "Mining Open Answers in Quessionare Data,''
IEEE Intelligent Systems. pp:58-63、September/October, 2002.
K.Yamanishi: "A Decision-theoretic Extension of Stochastic Complexity and Its Applications to Learning,''
IEEE Transactions on Information Theory, vol.44, 4, p.1424-1439, 1998.
K.Yamanishi: "Distributed Cooperative Bayesian Learning Strategies,''
Information and Computation, vol.150, p.22-56, 1998.
K.Yamanishi: ;"On-line Maximum Likelihood Prediction with respect to General Loss Functions,''
Journal on Computer and System Sciences, 55, p.105-118, 1997.
K.Yamanishi: "Probably Almost Discriminative Learning,''
Machine Learning, vol.18, pp.23-50, 1995.
K.Yamanishi: "A Loss Bound Model for On-line Stochastic Prediction Algorithms,''
Information and Computation, vol.119, 1, pp.39-54, 1995.
H.Mamitsuka and K.Yamanishi: "alpha-Helix Region Prediction with Stochastic Rule learning,''
CABIOS, vol.11, no.4, p.399-411, 1995.
K.Yamanishi: "A Learning Criterion for Stochastic Rules,''
Machine Learning, vol.9, pp.165-203, 1992.
K.Yamanishi: "Learning Non-parametric Densities in Terms of Finite-Dimensional Parametric Hypotheses,''
IEICE Transactions, Inf.&Syst., vol.E75-D, no.4, July 1992.
山西健司: 「Fermat符号の構成と性能について」
電子情報通信学会論文誌A, vol. J72-A, no.3, p.597-607, Mar. 1989.
山西健司: 「2元Modular符号の漸近的性能について」
電子情報通信学会論文誌A, vol. J71-A, no.12, p.2171-2181, Dec. 1988. （論文賞受賞）
山西健司: 「だ円符号と超だ円符号に基づく高性能符号の導出について」
電子情報通信学会論文誌A, vol.J71-A, no.10, p.1936-1946, Oct. 1988.
2. 国内・国際会議録（査読付き）
S.Fukushima and K. Yamanishi: "Graph community augmentation with GMM-modeling 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”, Proceedings of ICDM2023, pp:319-328.DOI:10.1109/ICDM58522.2023.00041
S.Fukushima and K. Yamanishi: ""Balancing Summarization and Change Detection in Graph Streams", Proceedings of ICDM2023, pp:1025-1030.
R.Yuki, A.Suzuki, and K. Yamanishi: "Dimensionality and Curvature Selection of Graph Embedding using DNML Code-Length", Proceedings of ICDM2023, pp:1517-1522.
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:33268--33284, 2023.
R. Yuki, Y. Ike, and K. Yamanishi:"Dimensionality selection of hyperbolic graph embeddings using decomposed normalized maximum likelihood code length" Proceedings of IEEE ICDM 2022, pp:666-675. https://doi.org/10.1109/ICDM54844.2022.00077
S. Hirai and K. Yamansihi: "Detecting Gradual Structure Changes of Non-parametric Distributions via Kernel Complexity" Proceedings of IEEE International Conference on BigData, pp:17-27, 2021. https://doi.org/10.48550/arXiv.2302.12127
S. Fukushima, R. Kanai, and K. Yamansihi: "Graph Summarization with Latent Variable Probabilistic Models" Complex Networks & Their Applications X pp 428–440, 2021. DOI: 10.1007/978-3-030-93413-2_36
K.Ueda, Y. Ike, and K. Yamanishi:"Change detection with probabilistic models on persistence diagrams" Proceedings of IEEE ICDM 2022, pp:1191--1196. https://doi.org/10.1109/ICDM54844.2022.00153
A.Suzuki, A.Nitanda, j. Wang, L. Xu, K. Yamanishi, and M. Cavazza: "Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic" Proceedings of NeurIIPS 2021, pp:1243-1255, 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 SIGKDD Conference on Knowledge Discovery and Data Mining(KDD2021), pp:3826--2834, Auust 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 139:10011-10021, 2021.
S. Fukushima and K. Yamanishii: "Detecting Hierarchical change detection in latent variable models", publication for 2020 IEEE International Conference on Data Mining(ICDM2020), pp:1028-1033, 2020. DOI: 10.1109/ICDM50108.2020.00120
J. Huang, L. Xu, J. Wang, K. Yamansihi: "Discovering Latent Class Labels for Multi-Label Learning", publication at IJCAI-PRICAI 2020, pp:3058-3064, 2020.
DOI:10.24963/ijcai.2020/423
S. Hirai and K. Yamanishi: "Detecting model changes and their early signals using MDL change statistics", 2019 IEEE International Conference on BigData (BigData2019)、pp:83-94, 2019.
DOI:10.1109/BigData47090.2019.9005617
A. Suzuki, J. Wang, F.Tian, A.Nitanda, and K.Yamanishi: "Hyperbolic ordinal embedding", publication at Asian Conference on Machine Learning (ACML2019), PMLR 101:1065-1080, 2019.
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), 3229-3230, 2019.
DOI:10.1145/3292500.3332284
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:2278-2286, Aug.2019
DOI:10.1145/3292500.3330757
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:3719-3725, Aug. 2019
K. Miyaguchi and K. Yamanishi: Adaptive minimax regret against smooth logarithmic losses over high-dimensional l1-balls via envelope complexity,"Proceedings of AISTATS 2019, pp:3440-3448, Apr.2019
J. Wang, A. Suzuki, L. Xu, F. Tian, L. Yang, K. Yamanishi： Orderly subspace clustering", Proceedings of AAAI 2019, Vol.33 No.01:AAAI-19,IAAI-19,EAAI-20 Jan.2019
DOI:10.1609/aaai.v33i01.33015264
S. Hirai and K.Yamanishi: “Detecting latent structure uncertainty with structural entropy”, Proceedings of IEEE International Conference on BigData (BigData2018), Dec. 2018.
DOI: https://ieeexplore.ieee.org/abstract/document/8622283
H.Sugiura, T.Kiwaki, Y.Siamak, H.Murata, R.Asaoka,K.Yamanishi: “Estimating glaucomatous visual sensitivity from retinal thickness by using pattern-based regularization and visualization”, Proceedings of 2018 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2018), pp: 783-792, Aug.2018,
DOI:10.1145/3219819.3219866.
J. Wang, Feng Tian ,W. Liu ,X. Wang, W. Zhang, K. Yamanishi: “Ranking preserving nonnegative matrix factorization,” International Joint Conference on Artificial Intelligence(IJCAI2018),pp:2776-2782,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: 1211-1215, 2018.
R. Kaneko, K. Miyaguchi, and K. Yamanishi： "Detecting Changes in Streaming Data with Information-Theoretic Windowing," Proceedings of IEEE International Conference on Big Data (BigData2017), pp:646-655, 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:948-955,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 code-length," Proceedings of the 4th IEEE International Conference on Data Science and Advanced Analytics(DSAA2017), pp:716-725, 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:1165--1174, 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:2041--2050,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:723-731, 2017.
DOI: 10.1137/1.9781611974973.81
K.Yamanishi, K.Miyaguchi: "Detecting gradual changes from data streams using MDL-change statistics", Proceedings of 2016 IEEE International Conference on BigData (BigData2016),
pp:156-163, 2016.
DOI: 10.1109/BigData.2016.7840601.
A.Suzuki, K.Miyaguchi, K.Yamanishi: "Structure selection for convolutive
non-negative matrix factorization using normalized maximum likelihood
coding",
Proceedings of 2016 IEEE International Conference on Data Mining (ICDM2016),
pp:1221-1226, 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:51-60, Oct. 2016.
DOI: 10.1109/DSAA.2016.13.
T. Lee, S. Matsushima, and K. Yamanishi: ”Traffic risk mining using
partially ordered non-negative matrix factorization,” Proceedings of 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA 2016), pp:622-631, Oct. 2016.
DOI: 10.1109/DSAA.2016.71
A. Demachi, S. Matsushima, and K.Yamanishi: "Web Behavior Analysis Using
Sparse Non-Negative Matrix Factorization," Proceedings of 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA 2016), 574-583, Oct. 2016.
DOI: 10.1109/DSAA.2016.85.
Y.Ito, S.Oeda, and K.Yamanishi: “Rank selection for non-negative matrixfactorization
with normalized maximum likelihood coding." Proceedings of SIAM International Conference on Data Mining (SDM2016), pp:720-728, 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:93-104, 2016.
DOI: 10.5220/0005703900930104.
K.Miyaguchi and K.Yamanishi: On-line 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 MDL-based clustering.," in
Proceedings of 2015 ACM International Conference on Knowledge Discovery and
Data Mining (KDD2015), pp:1979--1988, 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:953--958, 2014. code
DOI: 10.1109/ICDM.2014.70
S.Maya, K.Morino, and K.Yamanishi:Predicting Glaucoma Progression using Multi-task Learning with Heterogeneous Features.," Proceedings of IEEE BigData 2014: pp:261--270,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.417-424.2014.
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:367-368, 2014.
S.Sato and K.Yamanishi:〝Graph partitioning change detection using tree-based clustering," Proceedings of IEEE International Conference on Data Mining(ICDM2013)，pp:1169-1174, 2013.
DOI:10.1109/BigData.2014.7004241.
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:1121--1126, 2013.
DOI:10.1109/ICDM.2013.93.
Y.Sakai and K.Yamanishi: 〝An NML-based Model Selection Criterion for General Relational Data Modeling," Proceedings of IEEE International Conference on Big Data (BigData 2013)， pp:421--429,Oct. 2013.
DOI: 10.1109/BigData.2013.6691603.
S.Oeda and K.Yamanishi: 〝Extracting Time-evolving Latent Skills from Examination Time Series."
In Proceedings of The Sixth International Conference on Educational Data Mining (EDM2013), Pp:340--341, 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: 280-289, 2012.
E.Sakurai and K. Yamanishi: 〝Comparison of Dynamic Model Selection with Infinite HMM for Statistical Model Change Detection." In Proceedings of IEEE Information Theory Workshop 2012 (ITW2012), pp: 302-306, 2012.
H.Kanazawa and K.Yamanishi: 〝An MDL-based Change-Detection Algorithm with Its Applications to Learning Piecewise Stationary Memoryless Sources." In Proceedings of IEEE Information Theory Workshop 2012 (ITW2012), pp. 562-566, 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:343-351, 2012.
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.1031–1035, 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 change-point detection using sequentially discounting normalized maximum likelihood coding." The 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining(PAKDD2011), 2011.
S.Hirose, K.Yamanishi, T.Nakata and 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.
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.
R.Fujimaki, T.Nakata, H.Tsukahara, A.Sato and 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.
広瀬、山西: 「隠れ変数マイニングとその異常行動検出への応用」2007情報論的学習理論ワークショップ予稿集（IBIS2007）, pp:205-211, 2007.
松村、森永、山西: 「分散協調学習を用いたトピック構造マイニング」2005情報論的学習理論ワークショップ予稿集（IBIS2005）, 2005.
松村、森永、山西: 「分散へテロ情報源からのグローバルトピック構造の学習」情報科学技術フォーラム、情報技術レター（FIT2005）, 2005.
K.Yamanishi and Y.Maruyama: "Dynamic Syslog Mining for Network Failure Monitoring." Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2005), pp: 499-508, ACM Press, 2005.
丸山、山西: 「動的モデル選択とそのコンピュータセキュリティへの応用」 2004情報論的学習理論ワークショップ予稿(IBIS2004), pp:15-22, 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
森永、山西: 「有限混合モデルを用いたトピック傾向の動的マイニング」2004情報論的学習理論ワークショップ予稿集(IBIS2004), pp:78-85, 2004.
S.Morinaga and K.Yamanishi: "Tracking Dynamics of Topic Trends Using a Finite Mixture Model," Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2004), ACM Press, 2004.
松永、山西:「情報理論的手法に基づく異常行動検出」第2回情報技術フォーラム、情報技術レター (FIT2003) , 2003.
松永、山西:「動的モデル選択とその異常行動検出への応用」2003情報論的学習理論ワークショップ予稿集（IBIS2003）, pp:277-282, 2003.
S.Morinaga, K.Yamanishi, J.Takeuchi: "Distirbuted Cooperative Mining for Information Consortia," Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD2003), ACM Press, 2003.
森永、山西、竹内:「複数の異なる情報源に対する分散協調マイニング」 2002情報論的学習理論ワークショップ予稿集（2002）,pp:162-165、2002.
竹内、山西:「忘却型学習アルゴリズムを用いた外れ値検出と変化点検出の統一的扱い」2002情報論的学習理論ワークショップ予稿集（2002）,pp:156-161、2002.
K.Yamanishi and J.Takeuchi: "A Unifying Approach to Detecting Outliers and Change-Points from Non-stationary 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: "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:389-394, 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:443-449, 2001.
李、山西:「統計的学習手法を用いた自由記述アンケートデータからのマイニング」 2001情報論的学習理論ワークショップ予稿集（IBIS2001）, pp:129-134, 2001.
山西、竹内:「オンライン忘却型学習アルゴリズムを用いた統計的外れ値検出」2000 情報論的学習理論ワークショップ予稿集(IBIS2000), 2000.
李、山西: 「線形結合モデルを用いた統計的語彙的トピック分析」 2000情報論的学習理論ワークショップ予稿集(IBIS2000), 2000.
K.Yamanishi, J.Takeuchi, G.Williams, and P.Milne: "On-line 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:320--324 2000.
H.Li and K.Yamanishi: "Topic Analysis Using a Finite Mixture Model." Proceedings of ACL Workshop on Very Large Corpora, 2000.
李、山西:「ESCに基づくテキスト分類」1999情報論的学習理論ワークショップ予稿集(IBIS99), pp.239-244, 1999.
K.Yamanishi: "Extended Stochastic Complexity and Minimax Relative Loss Analysis.'' Algorithmic Learning Theory: The Tenth International Conference, ALT'99, Proceedings, pp.26-38, 1999. (Invited)
H.Li and K.Yamanishi: "Text Classification Using ESC-Based Stochastic Decision Lists." Proceedings of International Conference on Information & Knowledge Management(CIKM99), pp.122-130, 1999.
李、山西:「ESCに基づくテキスト分類」1999情報論的学習理論ワークショップ予稿集(IBIS99), pp.239-244, 1999.
山西健司: "Extended Stochastic Complexity in Individual Sequence Analysis." 1999情報論的学習理論ワークショップ予稿集(IBIS99), pp.163-168, 1999.
李、山西:「ESCに基づくテキスト分類」1999情報論的学習理論ワークショップ予稿集(IBIS99), pp.239-244, 1999.
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.39-47, Morgan Kaufmann, 1997.
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.32-43, 1998.
K.Yamanishi: "Distributed Cooperative Bayesian Learning Strategies." Proceedings of the Tenth Annual Conference on Computational Learning Theory(COLT97), pp.250-262, 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.99-109, 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.83-90, 1995.
K.Yamanishi: "On-Line Maximum Likelihood Prediction with respect to General Loss Functions.'' Lecture Notes in Artificial Intelligence 904, Computational Learning Theory: Second European Conference, EuroCOLT'95, pp.84-98, Springer, 1995.
K.Yamanishi: "The Minimum L-complexity Algorithm and Its Applications to Learning Non-parametric Rules.'' Proceedings of the Seventh Annual ACM Workshop on Computational Learning Theory(COLT94), p.173-182, ACM Press, 1994.
K.Yamanishi: "On-Line Prediction Based on the Extended Stochastic Complexity.'' Presented at Workshop on Descriptional Complexity, organized by E.Pednault, Newbrunswick, NJ, 1994.
K.Yamanishi: "Generalized Stochastic Complexity and Its Applications to Learning.'' Proceedings of the 1994 Conference on Information Science and Systems, vol.2, pp.763-768, 1994.（Invited）
K.Yamanishi: "Learning Non-parametric Smooth Rules by Stochastic Rules with Finite Partitioning.'' Computational Learning Theory: EuroCOLT'93, pp.217-228, Oxford, 1994.
K.Yamanishi: "On Polynomial-time Probably Almost Discriminative Learnability.'' Proceedings of the Sixth Annual ACM Conference on Computational Learning Theory(COLT93), pp.94-100, ACM Press, 1993.
H.Mamitsuka and K.Yamanishi: "Protein Secondary Structure Prediction Based on Stochastic-Rule Learning.'' Proceedings of the Third Annual Workshop on Algorithmic Learning Theory(ALT92), pp.240-251, 1993.
H.Mamitsuka and K.Yamanishi: "Protein $\alpha$-Helix Region Prediction Based on Stochastic-Rule Learning.'' Proceedings of the Twenty-Six Annual Hawaii International Conference on System Sciences(ICSS93), p.659-668, IEEE Computer Society Press, 1993.
K.Yamanishi: "Learning Non-parametric Densities by Finite Dimensional Parametric Hypotheses.'' Proceedings of the Second Annual Workshop on Algorithmic Learning Theory(ALT92), pp.175-186, JSAI Press, 1992.
K.Yamanishi: "Probably Almost Discriminative Learning.'' Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory(COLT92), pp.164-171, ACM Press, 1992.
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: "A Loss Bound Model for On-Line Stochastic Prediction Strategies.'' Proceedings of the Fourth Annual Workshop on Computational Learning Theory(COLT91), pp.290-302, Morgan Kaufmann, 1991.
K.Yamanishi and A.Konagaya: "Learning Stochastic Motifs from Genetic Sequences.'' Proceedings of the Eighth International Workshop on Machine Learning(ML91), pp.467-471, Morgan Kaufmann, 1991.
K.Yamanishi: "A Learning Criterion for Stochastic Rules.'' Proceedings of the Third Annual Workshop on Computational Learning Theory(COLT90), pp.67-81, Morgan Kaufmann, 1990.
K.Yamanishi: "Inferring Optimal Decision Lists from Stochastic Data Using the Minimum Description Length Criterion.'' Proceedings of 1990 IEEE International Symposium on Information Theory(ISIT90), San Diego, CA, Jan. 1990.
K.Yamanishi: "On New Asymptotic Performance Evaluation of Binary Modular Codes.'' Proceedings of 1988 IEEE International Symposium on Information Theory (ISIT88), Kobe Japan, June 1988.
3. 招待記事・解説記事etc.
山西健司：「オンラインファースト」ーコロナ禍で進展した情報社会を元に戻さないために（第4章）、東大出版会
ISBN978-4-13-063457-1 2020年12月刊行.
山西健司：「変化点検知から変化予兆検知へ」数理科学2019年6月号pp:22-29.
山西健司: 「記述長最小原理の進化：基礎から最新の展開」, 電子情報通信学会Fundamental Reiew,
第10巻第3号, pp:186-194, 2017.
DOI:https://doi.org/10.1587/essfr.10.3_186
山西健司: 「複雑データからのディープナレッジの発見」, 日本機械学会誌, 2015 10月号.
118 巻 1163 号 p. 616-619
DOI:
https://doi.org/10.1299/jsmemag.118.1163_616
山西健司：「異常検知：外れ値検知と変化検知」, 日本信頼性学会誌、2015年5月号.
DOI:https://doi.org/10.11348/reajshinrai.37.3_134
山西健司: 「潜在的ダイナミクスの学習理論」, 電子情報通信学会誌、Vol.97, No.5,pp:422--425, 2014.
ISSN:0913-5693
山西健司「ビッグデータといかに向き合うか」, 金融財政事情2013年9月23日 号（3042 号）
ISSN:1345-3033
山西健司「ビッグデータの深層を斬り出す：学習理論がえぐるディープナレッジ」 Provision Summer 2013, No.78.
山西健司「ビッグデータ」 科学技術振興機構 研究開発の俯瞰報告書 電子通信分 野（２０１３） pp:248-262
山西、森永、松村:「CGMマイニングと知識構造化」情報処理 Vol.48 No.8、pp:830-836, Sept.2007.
森永、山西:「CGMデータマイニング」NEC Technical Journal, Vol.60 No.2, NEC Corporation, 2007.
K.Yamanishi and S.Morinaga: "Data Mining for Knowledge Organization,” NEC Journal of Advanced Technology、Vol.2, No.２, pp:129-136, 2005.
K.Yamanishi, J.Takeuchi, Y.Maruyama: "Data Mining for Security," NEC Journal of Advanced Technology、Vol.2, No.1, pp:63-69, 2005.
山西、竹内、丸山:「統計的異常検出3手法」情報処理, vol.46, No.1, pp:34-40, 2005．
T.Egawa, M.Kobayashi, K.Yamanishi, A.Arutaki and J.Namiki: "Dynamic Collaboration from Scientists Eyes," NEC Journal of Advanced Technology, pp:17-26, vol.1, No.1, 2004.
山西、竹内、丸山：「セキュリティマイニング」NEC Technical Journal, Special Issue on Security, vol.56, No.12, pp:41-45, NEC Corporation, 2003.
森永、山西:「テキストマイニングと自由記述データ分析への応用」計測と制御, Vol.41, No.5, pp:354-357,2002.
山西健司:「データ＆テキストマイニングの最新動向」応用数理, vol.12, No.4,p.7-22,2002.
N.Abe, K.Yamanishi,A.Nakamura, H.Mamitsuka, J.Takeuchi, and H.Li : "Distributed and Active Learning,''（RWCプロジェクト総括報告）The Foundations of Real-World Intelligence, Oct. 2001.
山西健司:「データとテキストのマイニング」計算工学, Oct. 2001.
竹内、山西:「データマイニングにおける統計的外れ値検出」応用数理, 2001.
山西健司:「情報論的学習理論の現状と展望」情報処理, vol.42, No.1, pp:9--15, 2001.
山西健司:「統計的モデル選択と機械学習」計測と制御, vol.38, p.420-426, 1999.
山西健司:「情報理論, 統計的推論 と機械学習」 計測と制御, vol.38, p.411-412, 1999.
山西健司:「拡張型確率的コンプレキシティと学習理論」応用数理, vol.8, No.3, p.14-29, 1998.
山西健司:「MDL原理」日本ファジイ学会誌, vol.10, No.1, p.43-50, 1998.
山西健司:「データ圧縮と学習」人工知能学会誌, p.204-215, vol.12 (2), 1997.
山西健司:「確率的コンプレキシティと学習理論」オペレーションズリサーチ, p.379-386, vol.41 (7), 1996.
山西健司:「MDL入門－計算論的学習理論の立場から」人工知能学会誌, p.435-442, vol 7(3), May 1992.
山西、韓:「MDL入門－情報理論の立場から」人工知能学会誌, p.427-434, vol 7(3), May 1992.
山西健司:「代数幾何学的符号理論」数理科学、No.303, pp:63-72, Sept. 1988.
4. 招待講演
「異常検知からリスク管理へ」東京理科大経営学研究科技術経営専攻 「情報アナリシス」 2022.12.23.
「異常予兆検知ー基礎理論とリスク管理への応用」ものづくり企業に役立つ応用数理手法の研究会 2022.12.12.
「潜在空間を高度活用したディープナレッジの発見」JST^RIKEN ＡIP合同公開シンポジウム 2021.12.1.
「予兆情報学ー情報論的学習理論に基づく変化予兆検知」人工知能学会全国大会 2021.6.10.
「予兆情報学ー情報論的学習理論に基づく変化予兆検知」名古屋大学予兆学研究会 2021.1.13.
「記述長最小原理・変化検知・予兆情報学」JST/CRDS連続セミナー 2020.12.9.
「微分的MDL変化統計量に基づく変化予兆検知とCOVID-19感染爆発解析への応用」キャノングローバル戦略研究所 CIGS経済・社会との分野横断的研究会「ビッグデータとCOVID-19」 2020.11.12.
「Social Distancingを維持したまま社会活動を実現する情報技術」オンラインシンポジウム「ポストコロナの新しい情報社会に向けた提言」 、2020.7.4.
「計算論的眼科学ー眼科学へのAI・データサイエンスからのアプローチ」TRF研究 会、招待講演、2019.10.23.
「データマイニングによる異常予兆検知と医療への応用」脳ドック学会総会、特別講演、2019.6.21.
「潜在知識を読み解くデータサイエンス」電子情報通信学会サービスコンピューティング研究会/MaDIS研究交流会 2019.5.31.
「眼科学におけるＡＩ・データサイエンスの活用事例」日本眼科学会 眼科AI・ビッグデータ研究会 2019.4.21.
「データマイニングによる異常検知」キャノングローバル戦略研究所 経済・社会の分野横断的研究会 2018.12.4.
K.Yamanishi: “MDL-based Gradual Model Change Detection,” Workshop on Information-Theoretic Methods for Science and Engineering (WITMSE 2017), September 13th, 2017.
K.Yamanishi: “Discovering deep knowledge from heterogenous data,” ACM-2017 International Conference on Data Mining, Communications and Information Technology(DMCIT 2017), May 26th, 2017.
「変化兆候検知技術と情報セキュリティ」情報セキュリティ大学院大学 水平ワークショップ「人工知能とセキュリティ」, 2017.1.27.
「データマイニングによる異常検知」新日鉄住金株式会社 第３回全社ソリューション事例発表大会 基調講演, 2016.12.19.
「記述長最小原理の進化：基礎から最近の発展まで」 「非線形観測による推定の新展開」広島市立大セミナー、2016.10.7.
K.Yamanishi: "Stochastic complexity for latent variable modeling," The 9th Workshop on Information-theoretic Methods in Science and Engineering (WITMSE), Helsinki Finland, pp:45-47, Sept.20, 2016.
複雑データからの潜在知識の発見」JST-NSF国際連携シンポジウム「ビッグデータ と人工知能/機械学習が創る新たな社会」, 2016.5.12
「進化するMDLーMDLの基礎から最近の発展ー」 電子情報通信学会全国大会予稿（招 待), 2016.3.17
「複雑データからの深い知識の発見」 東京大学：「人工知能への情報理工学の取り 組み」シンポジウム, 2016.3.14
「情報セキュリティのための異常検知技術」 日本銀行情報セキュリティシンポジウ ム, 2016.3.2
「データマイニングによる異常検知」東大シンポジウムー2020年の社会設計に向けて、東京、2015.6.12.
「複雑データからのディープナレッジの発見」情報処理学会全国大会、2015.
「複雑ビッグデータからのディープナレッジの発見」。電気学会「ナノエレクトロニクス集積化・応用技術」調査専門委員会主催講演会, July 2014.
「潜在空間からのディープナレッジの発見」、応用統計学会 2014年度大会予稿集pp:15--20, May 2014.
「潜在空間からのディープナレッジの発見」 暗号フロンティア研究会, March, 2014.
K.Yamanishi, E.Sakurai, and H.Kanazawa: "Change Detection, Hyperthesis Testing, and Data Compression." IProceedings of Fifth Workshop on Information-Theoretic Methods in Science and Engineering (WITMSE 2012), pp:27--30, 2012.
S.Hirai and K.Yamanishi: "Clustering change detection using Normalized Maximum Likelihood Coding." Proceedings of Fifth Workshop on Information-Theoretic Methods in Science and Engineering (WITMSE 2012), pp:31--32, 2012.
「ネットワーク時系列データからの潜在的構造変化検知」統計数理研究所 数学・数理科学と諸科学・産業との連携研究ワークショップ『ネットワーク型知識に関する機械学習的アプローチ』, Feb.2012.
「時系列データからの潜在的構造変化検知」電子情報通信学会 パターン認識・メディア理解研究会, Feb.2012
『情報論的学習理論の最近の発展―Latent Dynamics―』電子情報通信学会, 情報理論研究会, Nov.2011.
"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-潜在的構造変化検出の情報論的学習理論」第一回Latent Dynamics ワークショップ, June, 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.
「動的モデル選択とデータマイニングへの応用」IBM東京基礎研究所セミナー, 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.
「潜在するビジネス価値を掘り起こす-解析の数理から戦略の数理へ」情報処理学会全国大会, March, 2009.
「リスクマイニング－データマイニングの最新動向」データウェアハウス＆CRM Expo チュートリアル, Tokyo, Japan, June 2006.
「NECデータマイニングセンターと事例紹介」オペレーションズリサーチ学会ビジネスインテリジェンスグループ研究会Tokyo, Japan, Mar. 2006.
「安全情報活用のためのセキュリティマイニング」OSPG セミナー, Tokyo, Japan, Mar.2006.
「動的へテロデータのマイニングとCGMメディア分析への応用」第１回横幹連合シンポジウム,Tokyo, Japan, Dec.2006.
「データマイニングに基づくサイバー犯罪の検知」情報セキュリティとAIシンポジウム，Tokyo, Japan, Nov.2006.
「CGMメディアとセキュリティのマイニングと知識化」情報価値化知識化協議会ワークショップ, Tokyo, Japan, Dec.2006.
「データマイニングに基づくセキュリティ技術」人工知能学会人工知能セミナー「コンピュータセキュリティとAI」、2005.
「テキストマイニングとNLPビジネス」JEITA シンポジウム「自然言語とNLPビジネス」Tokyo, Japan, 2003.
「Webマイニング」JEITA Research Workshop, Tokyo, Japan, June.2003.
「テキストマイニング」情報科学技術フォーラム (FIT2003) , Sapporo, Japan, Nov.2003.
「データマイニングとセキュリティ」第17回人工知能学会全国大会、Tokyo, Japan, June.2002.
「セキュリティ/Webインテリジェンスを実現するデータマイニング」AIシンポジウム, Tokyo, Japan, April.2002.
"Detecting Anomalies and Change-points for Cyber Threat Analysis, " IEEE International Conference on Data Mining, Workshop on Data Mining for Cyber Threat Analysis, Maehashi, Japan, December, 2002.
「Webマイニングと情報論的学習理論―評判分析と異常ログ検出」第5回情報論的学習理論ワークショップ (IBIS2002), Shizuoka, Japan, Sept., 2002.
「データマイニングによる異常検知とネットワーク侵入検知への応用」電子情報通信学会ネットワーク研究会. Osaka Japan, June, 2002.
「Webマイニングと情報論的学習理論」情報科学シンポジウム、Tokyo Japan, Jan., 2002.
「テキストデータの統計モデルを用いたテキストマイニング」AIワークショップ, Tokyo, Japan, July 2001.
「情報論的学習理論に基づくデータ・テキストマイニング」人工知能学会AI基礎研究会, Hokuriku, Japan, Mar. 2001.
「データマイニング技術の最新動向とCRMへの応用」データウェアハウス＆CRM Expo. Tutorial, Tokyo Japan, June 2001.
「有限混合モデルを用いたオンライン教師なし外れ値検出」統計数理研究所シンポジウム『統計科学、ニューラルネットワーク、データマイニングの新しい統合に向けて』, Tokyo Japan, Nov. 2000.
「データマイニングとビジネスHPC」NEC/HPC Workshop, Tokyo Japan, Dec. 2000.
「情報論的学習理論－動向と関連トピック」システム制御情報学会シンポジウム, May. 2000.
「情報マイニングー不正検出とテキストマイニングを例に」統計数理研究所シンポジウム『統計科学とデータマイニング』, Tokyo Japan, October 1999.
"Extended Stochastic Complexity and Minimax Relative Loss Analysis,'' Algorithmic Learning Theory: The Tenth International Conference, ALT'99,1999.
「情報論的学習理論」統計数理研究所シンポジウム『統計的情報処理のための数理的方法ワークショップ』, Tokyo, Japan, Jan.1998.
「MDL基準から拡張型確率的コンプレキシティへ」電子情報通信学会全国大会, 1998.
「拡張型確率的コンプレキシティとその機械学習への応用」第1回情報論的学習理論ワークショップ, July.1998.
‘’Generalized Stochastic Complexity and Its Applications to Learning.'' 1994 Conference on Information Science and Systems, 1994.
"On Complexity of MDL Learning and Discrimination." 1993 IEEE Information Theory Workshop, 1993.
"A Statistical Approach to Computational Learning Theory."Third NEC Research Symposium, May,1992.
「MDLは何故良いか？」第5回人工知能学会全国大会, June 1991.
「計算論的学習理論とMDL原理」情報理論とその応用ワークショップ、1991.
"On New Asymptotic Performance Evaluation of Binary Modular Codes.'' Workshop on Coding Theory, Osaka Japan, June 1988.
「代数幾何的符号」組み合わせ理論とその応用ワークショップ, Tsukuba Japan, July 1987.