発表文献リスト

2017

Kohei Miyaguchi and Kenji Yamanishi: "On-line detection of continuous chanages in stochastic processes," accepted for International Journal of Data Science and Analytics.

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.

Kohei Miyaguchi, Shin Matsushima, Kenji Yamanishi: “Sparse graphical modeling via stochastic complexity,"  accepted for 2017 SIAM International Conference on Data Mining (SDM2017).

2016

Kenji Yamanishi and Kohei Miyaguchi: “Detecting gradual changes from data stream using MDL-change statistics,"  Proceedings of 2016 IEEE International Conference on BigData (IEEE BigData2016), pp:156-163, 2016.

Atsushi Suzuki, Kohei Miyaguchi, Kenji Yamanishi: “Structure Selection Convolutive Non-negative Matrix Factorization Using Normalized Maximum Likelihood Coding," Proceedings of IEEE International Conference on Data Mining (ICDM2016), pp:1221-1226, 2016.

Shinya Sugawara, Tianyi Wu, and Kenji Yamaishi: "A basket two-part model to analyze medical expenditure on interdependent multiple sectors," accepted for Statistical Methods in Medical Research.

Yoshitaro Yonamoto, Kai Morino and Kenji 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, 2016.

Taito Lee, Shin Matsushima, and Kenji 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, 2016.

Akihiro Demachi, Shin Matsushima, and Kenji 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, 2016.

Kohei Miyaguchi, Shin Matsushima, Kenji Yamanishi: "Stochastic complexity for sparse modeling,"  Proceedings of 2016 Workshop on Information-theoretic Methods for Science and Engineering(WITMSE2016), pp:24-25, 2016.

Kenji Yamanishi: "Stochastic complexity for latent variable modeling,"  Proceedings of 2016 Workshop on Information-theoretic Methods for Science and Engineering(WITMSE2016), pp:45-47, 2016.

Shiya Sugawara: "Firm-driven management of longevity risk: Analysis of lump-sum payments in the Japanese narsing home market,"  to appear in Journal of Economics and Management Strategy, 2016.

Shin Matsushima:  " Asynchronous feature extraction for large-scale linear predictors," Proceedings of ECML-PKDD 2016.

S. Sugawara and J. Nakamura: “Gatekeeper incentives and demand inducement: An empirical analysis of care managers in the Japanese Long-Term Care Insurance program” to appear in Journal of the Japan and International Economies, 2016.

中村二朗, 菅原慎矢: 「同居率減少という誤解 -チャイルドレス高齢者の増加と介護問題-」 季刊社会保障研究 2016年第51巻3,4号 pp: 355-358. 

Y.Ito, S.Oeda, and K.Yamanishi:  “Rank selection for non-negative matrix factorization
with normalized maximum likelihood coding."  Proceedings of SIAM International Conference on Data Mining (SDM2016), pp:720-728, Mar. 2016.

山西健司: 進化するMDL-MDLの基礎から最近の発展,  電子情報通信学会全国大会予稿(招待), 2016年3月.

森野佳生: 緑内障進行予測におけるMDLに基づく知識発見, 電子情報通信学会全国大会予稿(招待), 2016年3月. 

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.

2015 

K.Moriya, S.Matsushima, and K.Yamansihi:  “Traffic risk mining from heterogenous road statistics. " Proceedings of IEEE International Conference on Data Science and Advanced Analytics (DSAA2015), Oct.2015.

K.Miyaguchi and K.Yamanishi:  “On-line detection of continuous changes in stochastic processes. " Proceedings of IEEE International Conference on Data Science and Advanced Analytics (DSAA2015), Oct.2015.

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.

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  5月号.

S. Maya, K. Morino, H. Murata, R. Asaoka, and K. Yamanishi: “ Discovery of glaucoma progressive patterns using hierarchical MDL-based clustering."  Proceedings of  the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2015), pp:1979-1988. 

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.

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.

K.Tamura, Y.Kobayashi, Y.Ihara:   “Evolution of individual versus social learning on social networks", Interface 2015.

H. Kajino, A.Kishimoto, A.Botea, E.Daly and S.Kotoulas:  “Active Learning for Multi-relational Data Construction", in Proceedings of WWW2015.

Y.Hayashi and K.Yamanishi:  “Sequential network change detection with its applications to ad impact relation analysisData," Mining and Knowledge Discovery: Volume 29, Issue 1 (2015), Page 137-167

2014

Y.Sakai and K.Yamanishi: “Data Fusion Using Restricted Boltzman Machines"   in Proceedings of  the IEEE International Conference on Data Mining(ICDM2014).

S.Maya, K.Morino and K.Yamanishi: “Predicting Glaucoma Progression using Multi-task Learning with Heterogeneous Features,"  in Proceedings of the IEEE International Conference on BigData (BigData2014).

T.Watanabe and H.Kashima: “A Label Completion Approach to Crowd Approximation," in Proceedings of the 21st International Conference on Neural Information Processing(ICONIP2014).

H.Kajino, Y.Baba, H.Kashima: “Instance-privacy Preserving Crowdsourcing” in Proceedings of  The Second AAAI Conference on Human Computation and Crowdsourcing (HCOMP-2014).

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).

S.Oeda, Y.Ito, and K.Yamaishi: ”Extracting Latent Skills from Time Series of Asynchronous and Incomplete Examinations" in Proceedings of the 7th International Conference on Educational Data Mining EDM2014.

H.Kajino, H. Arai, H.Kashima:‟Preserving Worker Privacy in Crowdsourcing", to appear in Data Mining and Knowledge Discovery , ECMLPKDD 2014 special issue.

山西健司: 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, (招待講演).

山西健司:情報論的学習とデータマイニング(数理工学ライブラリー3)  朝倉書店.

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.

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.

2013

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.

Y.Hayashi and K.Yamanishi:"Sequential Network Change Detection with Its Applications to Ad Impact Relation Analysis, Data Mining and Knowledge Discovery.published on-line, Print ISSN 1384-5810, September, 2013,

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.

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.

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, 2013.

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.

梶野 洸,坪井祐太,佐藤一誠,鹿島久嗣: エキスパートによる訓練データとクラウドソーシングで作成した訓練データからの教師付き学習, 人工知能学会論文誌, Vol.28, No.3, pp.243-248, 2013. [in Japanese][paper]

梶野 洸, 荒井 ひろみ, 鹿島 久嗣: クラウドソーシングにおけるワーカープライバシを保護した品質管理, In Proceedings of the 5th Forum on Data Engineering and Information Management, 2013. [in Japanese][paper][slide][poster]

2012

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.

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.

Daisuke Kimura and Hisashi Kashima: Fast Computation of Subpath Kernel for Trees. In Proc. 29th International Conference on Machine Learning (ICML'12), pp. 393-400, Edinburgh, Scotland, June 2012.[PDF][spotlight][poster]

鹿島 久嗣, 梶野 洸: クラウドソーシングと機械学習, 人工知能学会誌, Vol. 27, No. 4, pp.381-388, 2012.

 2011

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.

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