2024
Z.Que, L.Xu, and K.Yamanishi: ”Luckiness Normalized Maximum Likelihood-based Change Detection for High-dimensional Graphical Models with Missing Data" in IEEE BigData 2024.
S. Fukushima and K. Yamanishi: "Graph Community Augmentation with GMM-based Modeling in Latent Space," in ICDM 2024.
Y.Li, L.Xu, and K.Yamanishi: "GMMDA: Gaussian Mixture Modeling of Graph in Latent Space for Graph Data Augmentation," Knowledge and Information Systems (KAIS), 2024.
T. Tsuchiya, S. Ito, and J. Honda: "Exploration by Optimization with Hybrid Regularizers: Logarithmic Regret with Adversarial Robustness in Partial Monitoring," In ICML 2024.
Y. Kuroki, A. Rumi, T. Tsuchiya, F. Vitale, and N. Cesa-Bianchi: "Best-of-Both-Worlds Algorithms for Linear Contextual Bandits," In Proceedings of AISTATS 2024.
2023
T. Tsuchiya, S. Ito, and J. Honda: "Stability-penalty-adaptive follow-the-regularized-leader: Sparsity, game-dependency, and best-of-both-worlds," In NeurIPS 2023.
N. Nishikawa, Y. Ike, and K. Yamanishi: "Adaptive Topological Features via Persistent Homology Filetering Learning for Point Clouds”, accepted for presentation at NeurIPS 2023.
Kenji Yamanishi: "Learning with the Minimum Description Length Principle," Springer/Nature, 2023.
Y. Li, L. Xu, and K. Yamanishi: "GMMDA: Gaussian Mixture Modeling of Graph in Latent Space for Graph Data Augmentation”, accepted for presentation at ICDM2023.
S.Fukushima and K. Yamanishi: "Balancing Summarization and Change Detection in Graph Streams", accepted for presentation at ICDM2023.
R.Yuki, A.Suzuki, and K. Yamanishi: "Dimensionality and Curvature Selection of Graph Embedding using DNML Code-Length", accepted for presentation at ICDM2023.
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 Dimension," Applied Intelligence, 2023, DOI : 10.1007/s10489-023-04780-5
A.Suzuki, A.Nitanda, T.Suzuki, J.Wang, F.Tian, K. Yamanishi: "Tight and fast generalization error bound of graph embedding in metric space," Proceedings of ICML 2023, 2023.
2022
S. Kyoya and K.Yamanishi: "Mixture Complexity and Its Application to Gradual Clustering Change Detection," Entropy, 24(10): 1407 2022.
K.Ueda, Y. Ike, and K. Yamanishi:"Change detection with probabilistic models on persistence diagrams", Proceedings of 2022 IEEE ICDM, pp:666-675.
R. Yuki, Y. Ike, and K. Yamanishi:"Dimensionality selection of hyperbolic graph embeddings using decomposed normalized maximum likelihood code length", Proceedings of 2022 IEEE ICDM, pp:1191--1196.
山西、久野、島田、峰松、井手：「異常検知からリスク管理へ」サイエンス社AＩ・データサイエンスライブラリ,2022.
C.Lin, L.Xu, and K.Yamanishi: "Network Change Detection based on Random Walk in Latent Space," IEEE Transactions on Knowledge and Data Engineering, 2022. DOI: 10.1109/TKDE.2022.3167062
2021
S.Kyoya and K.Yamanishi: "Summarizing Finite Mixture Model withOverlapping Quantification," Entropy, 23(11), 1503, 2021.
So Hirai and Kenji Yamansihi: "Detecting Gradual Structure Changes of Non-parametric Distributions via Kernel Complexity", Proceedings of IEEE BigData 2021, pp:17-27, 2021.
Shintaro Fukushima, Ryoga Kanai, and Kenji Yamansihi: "Graph Summarization with Latent Variable Probabilistic Models", Complex Networks 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 10-2 visual field from optical coherence tomography in glaucoma using deep learning corrected with 24-2/30-2 visual field" Translational Vision Science and Technology, Vol.10, 28, doi:https://doi.org/10.1167/tvst.10.13.28.
Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, and Marc Cavazza: "Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic", accepted for presentation at NeurIIPS 2021.
Kenji Yamanishi, Linchuan Xu, Ryo Yuki, Shintaro Fukushima, Chunahao 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.
Phum Thuc Hung and Kenji Yamanishi: "Word2vec Skip-gram Dimensionality Selection via Sequential Normalized Maximum Likelihood," Entropy 2021, 23(8), 997; https://doi.org/10.3390/e23080997t
Linchuan Xu, Ryo Asaoka, Taichi Kiwaki, Hiroshi Murata, Yuri Fujino, Kenji 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.
Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, and Marc Cavazza: "Generalization Error Bound for Hyperbolic Ordinal Embedding" Proceedings of 2021 International Conference on Machine Learning (ICML2021), 10011-10021 , 2021.
Atsushi Suzuki and Kenji 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
Jun Huang, Linchuan Zu, Kun Qian, Jing Wang and K.Yamanishi: "Multi-label Learning with Missing and Completely Unobservable Labels" Data Mining and Knowledge Discovery, 35(3): pp:1061-1086, 2021.
https://doi.org/10.1007/s10618-021-00743-x2021.
Atsushi Nitanda and Taiji Suzuki: "Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime," accepted for publication for International Conference on Learning Representations (ICLR), 2021.
Shun-ichi Amari, Jimmy Ba, Roger Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, and Ji Xu: "When Does Preconditioning Help or Hurt Generalization?," accepted for publication for International Conference on Learning Representations (ICLR), 2021.
2020
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, Jan-Feb 2021;4(1):78-88. doi: 10.1016/j.ogla.2020.08.002. Epub 2020 Aug 10.2020.
Shintaro Fukushima and Kenji Yamnaishi: "Detecting hierarchical changes in latent variable models" IEEE Conference on Data Mining (ICDM2020), pp:1028-1033, 2020.
DOI: 10.1109/ICDM50108.2020.00120
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: "A 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 2020. DOI: 10.1136/bjophthalmol-2019-315600
Linchuan Xu, Ryo Asaoka, Taichi Kiwaki, Hiroshi Murata, Yuri Fujino, Masato Matsuura, Yohei Hashimoto,Shotaro Asano, Atsuya Miki, Kazuhiko Mori, Yoko Ikeda, Takashi Kanamoto, Junkichi Yamagami, Kenji Inoue, Masaki Tanito, Kenji 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
Jun Huang, Linchuan Xu, Jing Wang, Lei Feng and Kenji Yamanishi: "Discovering Latent Class Labels for Multi-Label Learning," IJCAI-PRICAI 2020, pp:3058-3064, 2020.
DOI:10.24963/ijcai.2020/423.
Atsushi Nitanda and Taiji Suzuki: "Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees," accepted for publication for Artificial Intelligence and Statistics (AISTATS), 2020.
Makoto Okada, Kenji Yamanishi and Naoki Masuda: "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
Lee Taito, Shin Matsushima, and Kenji Yamanshi: "Grafting for Combinatorial Binary Model using Frequent Itemset Mining", Data Mining and Knowledge Discovery,34(1), pp:101-123,2019. DOI: doi.org/10.1007/s10618-019-00657-9
2019
Shintaro Fukushima and Kenji Yamanishi: " Detecting Metachanges in Data Streams from the Viewpoint of the MDL Principle," Entropy, 21(7)1134, 2019. DOI:10.3390/e21121134
Linchuan Xu, Jing Wang, Lifang He, Jiannong Cao, Xiaokai Wei, Philip S. Yu, Kenji 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.
So Hirai and Kenji Yamanishi: "Detecting Model Changes and their Early Signals Using MDL Change Statistics", 2019 IEEE International Conference on BigData (BigData2019), pp:84--93, 2019. DOI:10.1109/TIT.2019.2915237
Atsushi Suzuki, Jing Wang, Feng Tian, Atsushi Nitanda, and Kenji Yamanishi: "Hyperbolic Ordinal Embedding". Proceedingss of Asian Conference on Machine Learning (ACML2019), pp: 1065-1080, 2019
Satoshi Hara, Atsushi Nitanda, and Takanori Maehara: "Data Cleansing for Models Trained with SGD". accepted for publication at Neural Information Processing Systems (NeurIPS2019), 2019.
Atsushi Nitanda, Tomoya Murata, and Taiji Suzuki: "Sharp Characterization of Optimal Minibatch Size for Stochastic Finite Sum Convex Optimization". accepted for publication at IEEE International Conference on Data Mining (ICDM2019), 2019.
Jilles Vreeken and Kenji Yamanishi: "Modern MDL meets Data Mining Insights, Theory, and Practice., " Proceedings of 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2019), pp:3229-3230, 2019. DOI:10.1145/3292500.3332284
Yunhui Fu, Shin Matsushima, Kenji Yamanishi: "Model Selection for Non-negative Tensor Factorization with Minimum Description Length" Entropy, 21(7): 632 , 2019. DOI:10.3390/e21070632
Yuhui Zheng, Linchuan Xu, Taichi Kiwaki, JIng Wang, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi: "Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression" Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2019), pp:2278-2286, 2019. DOI:10.1145/3292500.3330757
Jing Wang, Linchuan Xu, Feng Tian, Atsushi Suzuki Changqing Zhang , Kenji Yamanishi : “Attributed Subspace Clustering," Proceedings of International Joint Conference on Artificial Intelligence(IJCAI2019), pp:3719-3725, 2019.
Kenji Yamanishi, Tianyi Wu, Shinya Sugawara, Makoto 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
Kohei Miyaguchi and Kenji Yamanishi: "Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional l1-Balls via Envelope Complexity", Proceedings of AISTATS 2019, pp:3440-3448.
Atsushi Nitanda and Taiji Suzuki: "Stochastic Gradient Descent with Exponential Convergence Rates of Expected Classification Errors", Proceedings of AISTATS 2019, pp:1417-1426.
Jing Wang, Atsushi Suzuki, Linchuan Xu, Feng Tian, Liang Yang, Kenji Yamanishi："Orderly Subspace Clustering", Proceedings of Thirty-third AAAI Conference on Artificial Intelligence (AAAI2019), pp:5264-5272, 2019.
2018
So Hirai and K.Yamanishi: “Detecting Latent Structure Uncertainty with Structural Entropy”, accepted for publication at IEEE International Conference on BigData (BigData2018), Dec. 2018.
Koichi Moriya, Shin Matsushima, Kenji Yamanishi: “Traffic Risk Mining from Heterogeneous Road Statistics", accepted for publications in IEEE Transactions on Intelligent Transportation Systems, vol 19(11), 3662-3675, 2018. DOI: 10.1109/TITS.2018.2856533, 2018.
Kohei Miyaguchi and Kenji Yamanishi: "High-dimensional Penalty Selection via Minimum Description Length Principle" Machine Learning, 107(8-10), pp:1283-1302, 2018.
Kenji Yamanishi and Shintato Fukushima: " Model Change Detection with the MDL Principle", IEEE Transactions on Information Theory, 64(9), pp: 6115-6126, 2018.
Siamak Yousefi, Taichi Kiwaki, Yuhui Zheng, Hiroki Suigura, Ryo Asaoka, Hiroshi Murata, Hans Lemij and Kenji 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.
Hiroki Sugiura, Taichi Kiwaki, Yousefi Siamak, Hiroshi Murata, Ryo Asaoka, and Kenji Yamanishi: "Estimating Glaucomatous Visual Sensitivity from Retinal Thickness by Using Pattern-Based Regularization and Visualization", Proceedings of ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD2018), pp: 783-792, 2018.
Jing Wang, Feng Tian ,Weiwei Liu ,Xiao Wang, Wenjie Zhang, Kenji Yamanishi: "Ranking Preserving Nonnegative Matrix Factorization", Proceedings of International Joint Conference on Artificial Intelligence (IJCAI2018), pp:2776-2782, 2018.
Atsushi Suzuki, and Kenji Yamanishi："Exact Calculation of Normalized Maximum Likelihood Code Length Using Fourier Analysis" Proceedings of IEEE Symposium on Information Theory (ISIT2018), pp: 1211-1215, 2018.
青山和浩 山西健司： 東京大学工学教程 システム工学 「知識システムI 知識の表現と学習」（丸善出版）, 2018.
2017
Ryoya Kaneko, Kohei Miyaguchi, and Kenji Yamanishi："Detecting Changes in Streaming Data with Information-Theoretic Windowing," Proceedings of 2017 IEEE International Conference on Big Data(BigData2017 ), pp: 646-655, 2017.
Tatsuru Kobayashi, Shin Matsushima, Lee Taito, and Kenji Yamanishi:"Discovering Potential Traffic Risk in Japan using Supervised Learning Approach," Proceedings of 2017 IEEE International Conference on Big Data (BigData2017), pp: 948-955, 2017.
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, 2017.
Shin Matsushima, Hyokun Yun, Xinhua Zhang, S.V.N. Vishwanathan: "Distributed Stochastic Optimization of the Regularized Risk via Saddle-point Problem," Proceedings of The European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Data Bases (ECML-PKDD2017), pp:460-476, 2017.
Kenji Yamanishi: "Gradual Model Change Detection via Descriptional Dimensionality", Proceedings of Workshop on Information-theoretic methods in Science and Engineering (WITMSE2017), p. 42, 2018.
Tianyi Wu, Shinya Sugawara, Kenji 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, 2017.
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, 2017.
Kohei Miyaguchi and Kenji Yamanishi: "On-line detection of continuous chanages in stochastic processes," International Journal of Data Science and Analytics, 3:pp:213-229, 2017.
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," Proceedings of 2017 SIAM International Conference on Data Mining (SDM2017), pp:723-731, 2017.
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.
