A Learning Criterion for Stochastic Rules. - Kenji Yamanishi (COLT, 1990)

Learning non-parametric densities by finite-dimensional parametric hypotheses. - Kenji Yamanishi (ALT, 1991)

A Loss Bound Model for On-Line Stochastic Prediction Strategies. - Kenji Yamanishi (COLT, 1991)

Learning Stochastic Motifs from Genetic Sequences. - Kenji Yamanishi; Akihiko Konagaya (ML, 1991)

Protein Secondary Structure Prediction Based on Stochastic-Rule Learning. - Hiroshi Mamitsuka; Kenji Yamanishi (ALT, 1992)

Probably Almost Discriminative Learning. - Kenji Yamanishi (COLT, 1992)

On Polynomial-Time Probably almost Discriminative Learnability. - Kenji Yamanishi (COLT, 1993)

Learning non-parametric smooth rules by stochastic rules with finite partitioning. - Kenji Yamanishi (EuroCOLT, 1993)

The Minimum L-Complexity Algorithm and its Applications to Learning Non-Parametric Rules. - Kenji Yamanishi (COLT, 1994)

Randomized Approximate Aggregating Strategies and Their Applications to Prediction and Discrimination. - Kenji Yamanishi (COLT, 1995)

On-line maximum likelihood prediction with respect to general loss functions. - Kenji Yamanishi (EuroCOLT, 1995)

alpha-Helix region prediction with stochastic rule learning. - Hiroshi Mamitsuka; Kenji Yamanishi (Comput. Appl. Biosci., 1995)

A Loss Bound Model for On-Line Stochastic Prediction Algorithms - Kenji Yamanishi (Inf. Comput., 1995)

A Randomized Approximation of the MDL for Stochastic Models with Hidden Variables. - Kenji Yamanishi (COLT, 1996)

Document Classification Using a Finite Mixture Model. - Hang Li 011; Kenji Yamanishi (ACL, 1997)

Distributed Cooperative Bayesian Learning Strategies. - Kenji Yamanishi (COLT, 1997)

Minimax Relative Loss Analysis for Sequential Prediction Algorithms Using Parametric Hypotheses. - Kenji Yamanishi (COLT, 1998)

A Decision-Theoretic Extension of Stochastic Complexity and Its Applications to Learning. - Kenji Yamanishi (IEEE Trans. Inf. Theory, 1998)

Extended Stochastic Complexity and Minimax Relative Loss Analysis. - Kenji Yamanishi (ALT, 1999)

Text Classification Using ESC-based Stochastic Decision Lists. - Hang Li 011; Kenji Yamanishi (CIKM, 1999)

Topic Analysis Using a Finite Mixture Model. - Hang Li 011; Kenji Yamanishi (EMNLP, 2000)

On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. - Kenji Yamanishi; Jun'ichi Takeuchi; Graham J. Williams; Peter Milne (KDD, 2000)

Mining from open answers in questionnaire data. - Hang Li 011; Kenji Yamanishi (KDD, 2001)

Discovering outlier filtering rules from unlabeled data: combining a supervised learner with an unsupervised learner. - Kenji Yamanishi; Jun'ichi Takeuchi (KDD, 2001)

Mining product reputations on the Web. - Satoshi Morinaga; Kenji Yamanishi; Kenji Tateishi; Toshikazu Fukushima (KDD, 2002)

A unifying framework for detecting outliers and change points from non-stationary time series data. - Kenji Yamanishi; Jun'ichi Takeuchi (KDD, 2002)

Mining Open Answers in Questionnaire Data. - Kenji Yamanishi; Hang Li 011 (IEEE Intell. Syst., 2002)

Distributed cooperative mining for information consortia. - Satoshi Morinaga; Kenji Yamanishi; Jun'ichi Takeuchi (KDD, 2003)

Dynamic model selection with its applications to computer security. - Yuko Maruyama; Kenji Yamanishi (ITW, 2004)

Tracking dynamics of topic trends using a finite mixture model. - Satoshi Morinaga; Kenji Yamanishi (KDD, 2004)

Dynamic syslog mining for network failure monitoring. - Kenji Yamanishi; Yuko Maruyama (KDD, 2005)

A Unifying Framework for Detecting Outliers and Change Points from Time Series. - Jun'ichi Takeuchi; Kenji Yamanishi (IEEE Trans. Knowl. Data Eng., 2006)

Dynamic Model Selection With its Applications to Novelty Detection. - Kenji Yamanishi; Yuko Maruyama (IEEE Trans. Inf. Theory, 2007)

Latent Variable Mining with Its Applications to Anomalous Behavior Detection. - Shunsuke Hirose; Kenji Yamanishi (SDM, 2008)

Network anomaly detection based on Eigen equation compression. - Shunsuke Hirose; Kenji Yamanishi; Takayuki Nakata; Ryohei Fujimaki (KDD, 2009)

Mining abnormal patterns from heterogeneous time-series with irrelevant features for fault event detection. - Ryohei Fujimaki; Takayuki Nakata; Hidenori Tsukahara; Akinori Sato; Kenji Yamanishi (Stat. Anal. Data Min., 2009)

Discovering Emerging Topics in Social Streams via Link Anomaly Detection. - Toshimitsu Takahashi; Ryota Tomioka; Kenji Yamanishi (ICDM, 2011)

Efficient computation of normalized maximum likelihood coding for Gaussian mixtures with its applications to optimal clustering. - So Hirai; Kenji Yamanishi (ISIT, 2011)

Real-Time Change-Point Detection Using Sequentially Discounting Normalized Maximum Likelihood Coding. - Yasuhiro Urabe; Kenji Yamanishi; Ryota Tomioka; Hiroki Iwai (PAKDD (2), 2011)

Sequential Network Change Detection with Its Applications to Ad Impact Relation Analysis. - Yu Hayashi; Kenji Yamanishi (ICDM, 2012)

An MDL-based change-detection algorithm with its applications to learning piecewise stationary memoryless sources. - Hiroki Kanazawa; Kenji Yamanishi (ITW, 2012)

Comparison of dynamic model selection with infinite HMM for statistical model change detection. - Eiichi Sakurai; Kenji Yamanishi (ITW, 2012)

Detecting changes of clustering structures using normalized maximum likelihood coding. - So Hirai; Kenji Yamanishi (KDD, 2012)

Normalized Maximum Likelihood Coding for Exponential Family with Its Applications to Optimal Clustering - So Hirai; Kenji Yamanishi (CoRR, 2012)

An NML-based model selection criterion for general relational data modeling. - Yoshiki Sakai; Kenji Yamanishi (IEEE BigData, 2013)

Extracting Time-evolving Latent Skills from Examination Time Series. - Shinichi Oeda; Kenji Yamanishi (EDM, 2013)

Quantitative Prediction of Glaucomatous Visual Field Loss from Few Measurements. - Zenghan Liang; Ryota Tomioka; Hiroshi Murata; Ryo Asaoka; Kenji Yamanishi (ICDM, 2013)

Graph Partitioning Change Detection Using Tree-Based Clustering. - Sho-Ichi Sato; Kenji Yamanishi (ICDM, 2013)

Efficient Computation of Normalized Maximum Likelihood Codes for Gaussian Mixture Models With Its Applications to Clustering. - So Hirai; Kenji Yamanishi (IEEE Trans. Inf. Theory, 2013)

Early detection of persistent topics in social networks. - Shota Saito; Ryota Tomioka; Kenji Yamanishi (ASONAM, 2014)

Predicting glaucoma progression using multi-task learning with heterogeneous features. - Shigeru Maya; Kai Morino; Kenji Yamanishi (IEEE BigData, 2014)

Extracting Latent Skills from Time Series of Asynchronous and Incomplete Examinations. - Shinichi Oeda; Yu Ito; Kenji Yamanishi (EDM, 2014)

Data Fusion Using Restricted Boltzmann Machines. - Yoshiki Sakai; Kenji Yamanishi (ICDM, 2014)

On-line detection of continuous changes in stochastic processes. - Kohei Miyaguchi; Kenji Yamanishi (DSAA, 2015)

Traffic risk mining from heterogeneous road statistics. - Koichi Moriya; Shin Matsushima; Kenji Yamanishi (DSAA, 2015)

Discovery of Glaucoma Progressive Patterns Using Hierarchical MDL-Based Clustering. - Shigeru Maya; Kai Morino; Hiroshi Murata; Ryo Asaoka; Kenji Yamanishi (KDD, 2015)

Detecting gradual changes from data stream using MDL-change statistics. - Kenji Yamanishi; Kohei Miyaguchi (IEEE BigData, 2016)

Predicting Glaucomatous Progression with Piecewise Regression Model from Heterogeneous Medical Data. - Kyosuke Tomoda; Kai Morino; Hiroshi Murata; Ryo Asaoka; Kenji Yamanishi (HEALTHINF, 2016)

Web Behavior Analysis Using Sparse Non-Negative Matrix Factorization. - Akihiro Demachi; Shin Matsushima; Kenji Yamanishi (DSAA, 2016)

Traffic Risk Mining Using Partially Ordered Non-Negative Matrix Factorization. - Taito Lee; Shin Matsushima; Kenji Yamanishi (DSAA, 2016)

Temporal Network Change Detection Using Network Centralities. - Yoshitaro Yonamoto; Kai Morino; Kenji Yamanishi (DSAA, 2016)

Structure Selection for Convolutive Non-negative Matrix Factorization Using Normalized Maximum Likelihood Coding. - Atsushi Suzuki; Kohei Miyaguchi; Kenji Yamanishi (ICDM, 2016)

Rank Selection for Non-negative Matrix Factorization with Normalized Maximum Likelihood Coding. - Yu Ito; Shinichi Oeda; Kenji Yamanishi (SDM, 2016)

Predicting Glaucoma Visual Field Loss by Hierarchically Aggregating Clustering-based Predictors. - Motohide Higaki; Kai Morino; Hiroshi Murata; Ryo Asaoka; Kenji Yamanishi (CoRR, 2016)

Detecting changes in streaming data with information-theoretic windowing. - Ryoya Kaneko; Kohei Miyaguchi; Kenji Yamanishi (IEEE BigData, 2017)

Discovering potential traffic risks in Japan using a supervised learning approach. - Tatsuru Kobayashi; Shin Matsushima; Taito Lee; Kenji Yamanishi (IEEE BigData, 2017)

Latent Dimensionality Estimation for Probabilistic Canonical Correlation Analysis Using Normalized Maximum Likelihood Code-Length. - Tomohiko Nakmaura; Tomoharu Iwata; Kenji Yamanishi (DSAA, 2017)

Multi-view Learning over Retinal Thickness and Visual Sensitivity on Glaucomatous Eyes. - Toshimitsu Uesaka; Kai Morino; Hiroki Sugiura; Taichi Kiwaki; Hiroshi Murata; Ryo Asaoka; Kenji Yamanishi (KDD, 2017)

Decomposed Normalized Maximum Likelihood Codelength Criterion for Selecting Hierarchical Latent Variable Models. - Tianyi Wu; Shinya Sugawara; Kenji Yamanishi (KDD, 2017)

Sparse Graphical Modeling via Stochastic Complexity. - Kohei Miyaguchi; Shin Matsushima; Kenji Yamanishi (SDM, 2017)

An Upper Bound on Normalized Maximum Likelihood Codes for Gaussian Mixture Models. - So Hirai; Kenji Yamanishi (CoRR, 2017)

Grafting for Combinatorial Boolean Model using Frequent Itemset Mining. - Taito Lee; Shin Matsushima; Kenji Yamanishi (CoRR, 2017)

Online detection of continuous changes in stochastic processes. - Kohei Miyaguchi; Kenji Yamanishi (Int. J. Data Sci. Anal., 2017)

Detecting Latent Structure Uncertainty with Structural Entropy. - So Hirai; Kenji Yamanishi (IEEE BigData, 2018)

Ranking Preserving Nonnegative Matrix Factorization. - Jing Wang 023; Feng Tian 006; Weiwei Liu 003; Xiao Wang 017; Wenjie Zhang 001; Kenji Yamanishi (IJCAI, 2018)

Exact Calculation of Normalized Maximum Likelihood Code Length Using Fourier Analysis. - Atsushi Suzuki; Kenji Yamanishi (ISIT, 2018)

Estimating Glaucomatous Visual Sensitivity from Retinal Thickness with Pattern-Based Regularization and Visualization. - Hiroki Sugiura; Taichi Kiwaki; Siamak Yousefi; Hiroshi Murata; Ryo Asaoka; Kenji Yamanishi (KDD, 2018)

Stable Geodesic Update on Hyperbolic Space and its Application to Poincare Embeddings. - Yosuke Enokida; Atsushi Suzuki; Kenji Yamanishi (CoRR, 2018)

Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional ε1-Balls via Envelope Complexity. - Kohei Miyaguchi; Kenji Yamanishi (CoRR, 2018)

High-dimensional penalty selection via minimum description length principle. - Kohei Miyaguchi; Kenji Yamanishi (Mach. Learn., 2018)

Model Change Detection With the MDL Principle. - Kenji Yamanishi; Shintaro Fukushima (IEEE Trans. Inf. Theory, 2018)

Orderly Subspace Clustering. - Jing Wang 023; Atsushi Suzuki; Linchuan Xu; Feng Tian 006; Liang Yang 002; Kenji Yamanishi (AAAI, 2019)

Hyperbolic Ordinal Embedding. - Atsushi Suzuki; Jing Wang 023; Feng Tian 006; Atsushi Nitanda; Kenji Yamanishi (ACML, 2019)

Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional l1-Balls via Envelope Complexity. - Kohei Miyaguchi; Kenji Yamanishi (AISTATS, 2019)

Detecting Model Changes and their Early Warning Signals Using MDL Change Statistics. - So Hirai; Kenji Yamanishi (IEEE BigData, 2019)

Attributed Subspace Clustering. - Jing Wang 023; Linchuan Xu; Feng Tian 006; Atsushi Suzuki; Changqing Zhang 002; Kenji Yamanishi (IJCAI, 2019)

Modern MDL meets Data Mining Insights, Theory, and Practice. - Jilles Vreeken; Kenji Yamanishi (KDD, 2019)

Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression. - Yuhui Zheng; Linchuan Xu; Taichi Kiwaki; Jing Wang 023; Hiroshi Murata; Ryo Asaoka; Kenji Yamanishi (KDD, 2019)

Descriptive Dimensionality and Its Characterization of MDL-based Learning and Change Detection. - Kenji Yamanishi (CoRR, 2019)

The decomposed normalized maximum likelihood code-length criterion for selecting hierarchical latent variable models. - Kenji Yamanishi; Tianyi Wu; Shinya Sugawara; Makoto Okada (Data Min. Knowl. Discov., 2019)

Model Selection for Non-Negative Tensor Factorization with Minimum Description Length. - Yunhui Fu; Shin Matsushima; Kenji Yamanishi (Entropy, 2019)

Detecting Metachanges in Data Streams from the Viewpoint of the MDL Principle. - Shintaro Fukushima; Kenji Yamanishi (Entropy, 2019)

Correction to Efficient Computation of Normalized Maximum Likelihood Codes for Gaussian Mixture Models With Its Applications to Clustering. - So Hirai; Kenji Yamanishi (IEEE Trans. Inf. Theory, 2019)

Detecting Hierarchical Changes in Latent Variable Models. - Shintaro Fukushima; Kenji Yamanishi (ICDM, 2020)

Discovering Latent Class Labels for Multi-Label Learning. - Jun Huang 003; Linchuan Xu; Jing Wang 023; Lei Feng 006; Kenji Yamanishi (IJCAI, 2020)

Online Robust and Adaptive Learning from Data Streams. - Shintaro Fukushima; Atsushi Nitanda; Kenji Yamanishi (CoRR, 2020)

Change Sign Detection with Differential MDL Change Statistics and its Applications to COVID-19 Pandemic Analysis. - Kenji Yamanishi; Linchuan Xu; Ryo Yuki; Shintaro Fukushima; Chuan-hao Lin (CoRR, 2020)

A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification. - Linchuan Xu; Jun Huang 003; Atsushi Nitanda; Ryo Asaoka; Kenji Yamanishi (CoRR, 2020)

Grafting for combinatorial binary model using frequent itemset mining. - Taito Lee; Shin Matsushima; Kenji Yamanishi (Data Min. Knowl. Discov., 2020)

Detecting Gradual Structure Changes of Non-parametric Distributions via Kernel Complexity. - So Hirai; Kenji Yamanishi (IEEE BigData, 2021)

Graph Summarization with Latent Variable Probabilistic Models. - Shintaro Fukushima; Ryoga Kanai; Kenji Yamanishi (COMPLEX NETWORKS, 2021)

Generalization Error Bound for Hyperbolic Ordinal Embedding. - Atsushi Suzuki; Atsushi Nitanda; Jing Wang 023; Linchuan Xu; Kenji Yamanishi; Marc Cavazza (ICML, 2021)

PAMI: A Computational Module for Joint Estimation and Progression Prediction of Glaucoma. - Linchuan Xu; Ryo Asaoka; Taichi Kiwaki; Hiroshi Murata; Yuri Fujino; Kenji Yamanishi (KDD, 2021)

Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic. - Atsushi Suzuki; Atsushi Nitanda; Jing Wang 023; Linchuan Xu; Kenji Yamanishi; Marc Cavazza (NeurIPS, 2021)

Multi-label learning with missing and completely unobserved labels. - Jun Huang 003; Linchuan Xu; Kun Qian 003; Jing Wang 023; Kenji Yamanishi (Data Min. Knowl. Discov., 2021)

Word2vec Skip-Gram Dimensionality Selection via Sequential Normalized Maximum Likelihood. - Pham Thuc Hung; Kenji Yamanishi (Entropy, 2021)

Summarizing Finite Mixture Model with Overlapping Quantification. - Shunki Kyoya; Kenji Yamanishi (Entropy, 2021)

Fourier-Analysis-Based Form of Normalized Maximum Likelihood: Exact Formula and Relation to Complex Bayesian Prior. - Atsushi Suzuki; Kenji Yamanishi (IEEE Trans. Inf. Theory, 2021)

MixSp: A Framework for Embedding Heterogeneous Information Networks With Arbitrary Number of Node and Edge Types. - Linchuan Xu; Jing Wang 023; Lifang He 001; Jiannong Cao 001; Xiaokai Wei; Philip S. Yu; Kenji Yamanishi (IEEE Trans. Knowl. Data Eng., 2021)

Change Detection with Probabilistic Models on Persistence Diagrams. - Kohei Ueda; Yuichi Ike; Kenji Yamanishi (ICDM, 2022)

Dimensionality Selection of Hyperbolic Graph Embeddings using Decomposed Normalized Maximum Likelihood Code-Length. - Ryo Yuki; Yuichi Ike; Kenji Yamanishi (ICDM, 2022)

Mixture Complexity and Its Application to Gradual Clustering Change Detection. - Shunki Kyoya; Kenji Yamanishi (Entropy, 2022)

Learning with the Minimum Description Length Principle - Kenji Yamanishi (Springer, 2023)

Balancing Summarization and Change Detection in Graph Streams. - Shintaro Fukushima; Kenji Yamanishi (ICDM, 2023)

GMMDA: Gaussian Mixture Modeling of Graph in Latent Space for Graph Data Augmentation. - Yanjin Li; Linchuan Xu; Kenji Yamanishi (ICDM, 2023)

Dimensionality and Curvature Selection of Graph Embedding using Decomposed Normalized Maximum Likelihood Code-Length. - Ryo Yuki; Atsushi Suzuki; Kenji Yamanishi (ICDM, 2023)

Tight and fast generalization error bound of graph embedding in metric space. - Atsushi Suzuki; Atsushi Nitanda; Taiji Suzuki; Jing Wang 023; Feng Tian 006; Kenji Yamanishi (ICML, 2023)

Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds. - Naoki Nishikawa; Yuichi Ike; Kenji Yamanishi (NeurIPS, 2023)

Network Change Detection Based on Random Walk in Latent Space. - Chuan-hao Lin; Linchuan Xu; Kenji Yamanishi (IEEE Trans. Knowl. Data Eng., 2023)

Detecting signs of model change with continuous model selection based on descriptive dimensionality. - Kenji Yamanishi; So Hirai (Appl. Intell., 2023)

Dimensionality selection for hyperbolic embeddings using decomposed normalized maximum likelihood code-length. - Ryo Yuki; Yuichi Ike; Kenji Yamanishi (Knowl. Inf. Syst., 2023)

Luckiness Normalized Maximum Likelihood-based Change Detection for High-dimensional Graphical Models with Missing Data. - Zhongyi Que; Linchuan Xu; Kenji Yamanishi (IEEE Big Data, 2024)

Graph Community Augmentation with GMM-Based Modeling in Latent Space. - Shintaro Fukushima; Kenji Yamanishi (ICDM, 2024)

Clustering Change Sign Detection by Fusing Mixture Complexity. - Kento Urano; Ryo Yuki; Kenji Yamanishi (CoRR, 2024)

Foundation of Calculating Normalized Maximum Likelihood for Continuous Probability Models. - Atsushi Suzuki; Kota Fukuzawa; Kenji Yamanishi (CoRR, 2024)

Bandit and Delayed Feedback in Online Structured Prediction. - Yuki Shibukawa; Taira Tsuchiya; Shinsaku Sakaue; Kenji Yamanishi (CoRR, 2025)

Normalized Maximum Likelihood Code-Length on Riemannian Manifold Data Spaces. - Kota Fukuzawa; Atsushi Suzuki; Kenji Yamanishi (CoRR, 2025)