| 2024 | ICDM | Graph Community Augmentation with GMM-Based Modeling in Latent Space. | Shintaro Fukushima, Kenji Yamanishi |
| 2023 | ICDM | Balancing Summarization and Change Detection in Graph Streams. | Shintaro Fukushima, Kenji Yamanishi |
| 2023 | ICDM | GMMDA: Gaussian Mixture Modeling of Graph in Latent Space for Graph Data Augmentation. | Yanjin Li, Linchuan Xu, Kenji Yamanishi |
| 2023 | ICDM | Dimensionality and Curvature Selection of Graph Embedding using Decomposed Normalized Maximum Likelihood Code-Length. | Ryo Yuki, Atsushi Suzuki, Kenji Yamanishi |
| 2023 | ICML | Tight and fast generalization error bound of graph embedding in metric space. | Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian, Kenji Yamanishi |
| 2022 | ICDM | Change Detection with Probabilistic Models on Persistence Diagrams. | Kohei Ueda, Yuichi Ike, Kenji Yamanishi |
| 2022 | ICDM | Dimensionality Selection of Hyperbolic Graph Embeddings using Decomposed Normalized Maximum Likelihood Code-Length. | Ryo Yuki, Yuichi Ike, Kenji Yamanishi |
| 2021 | ICML | Generalization Error Bound for Hyperbolic Ordinal Embedding. | Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza |
| 2021 | KDD | PAMI: A Computational Module for Joint Estimation and Progression Prediction of Glaucoma. | Linchuan Xu, Ryo Asaoka, Taichi Kiwaki, Hiroshi Murata, Yuri Fujino, Kenji Yamanishi |
| 2020 | ICDM | Detecting Hierarchical Changes in Latent Variable Models. | Shintaro Fukushima, Kenji Yamanishi |
| 2020 | IJCAI | Discovering Latent Class Labels for Multi-Label Learning. | Jun Huang, Linchuan Xu, Jing Wang, Lei Feng, Kenji Yamanishi |
| 2019 | AAAI | Orderly Subspace Clustering. | Jing Wang, Atsushi Suzuki, Linchuan Xu, Feng Tian, Liang Yang, Kenji Yamanishi |
| 2019 | ACML | Hyperbolic Ordinal Embedding. | Atsushi Suzuki, Jing Wang, Feng Tian, Atsushi Nitanda, Kenji Yamanishi |
| 2019 | AISTATS | Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional l1-Balls via Envelope Complexity. | Kohei Miyaguchi, Kenji Yamanishi |
| 2019 | IJCAI | Attributed Subspace Clustering. | Jing Wang, Linchuan Xu, Feng Tian, Atsushi Suzuki, Changqing Zhang, Kenji Yamanishi |
| 2019 | KDD | Modern MDL meets Data Mining Insights, Theory, and Practice. | Jilles Vreeken, Kenji Yamanishi |
| 2019 | KDD | Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression. | Yuhui Zheng, Linchuan Xu, Taichi Kiwaki, Jing Wang, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi |
| 2018 | IJCAI | Ranking Preserving Nonnegative Matrix Factorization. | Jing Wang, Feng Tian, Weiwei Liu, Xiao Wang, Wenjie Zhang, Kenji Yamanishi |
| 2018 | ISIT | Exact Calculation of Normalized Maximum Likelihood Code Length Using Fourier Analysis. | Atsushi Suzuki, Kenji Yamanishi |
| 2018 | KDD | 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 |
| 2017 | DSAA | Latent Dimensionality Estimation for Probabilistic Canonical Correlation Analysis Using Normalized Maximum Likelihood Code-Length. | Tomohiko Nakmaura, Tomoharu Iwata, Kenji Yamanishi |
| 2017 | KDD | 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 |
| 2017 | KDD | Decomposed Normalized Maximum Likelihood Codelength Criterion for Selecting Hierarchical Latent Variable Models. | Tianyi Wu, Shinya Sugawara, Kenji Yamanishi |
| 2017 | SDM | Sparse Graphical Modeling via Stochastic Complexity. | Kohei Miyaguchi, Shin Matsushima, Kenji Yamanishi |
| 2016 | DSAA | Web Behavior Analysis Using Sparse Non-Negative Matrix Factorization. | Akihiro Demachi, Shin Matsushima, Kenji Yamanishi |
| 2016 | DSAA | Traffic Risk Mining Using Partially Ordered Non-Negative Matrix Factorization. | Taito Lee, Shin Matsushima, Kenji Yamanishi |
| 2016 | DSAA | Temporal Network Change Detection Using Network Centralities. | Yoshitaro Yonamoto, Kai Morino, Kenji Yamanishi |
| 2016 | ICDM | Structure Selection for Convolutive Non-negative Matrix Factorization Using Normalized Maximum Likelihood Coding. | Atsushi Suzuki, Kohei Miyaguchi, Kenji Yamanishi |
| 2016 | SDM | Rank Selection for Non-negative Matrix Factorization with Normalized Maximum Likelihood Coding. | Yu Ito, Shinichi Oeda, Kenji Yamanishi |
| 2015 | DSAA | On-line detection of continuous changes in stochastic processes. | Kohei Miyaguchi, Kenji Yamanishi |
| 2015 | DSAA | Traffic risk mining from heterogeneous road statistics. | Koichi Moriya, Shin Matsushima, Kenji Yamanishi |
| 2015 | KDD | Discovery of Glaucoma Progressive Patterns Using Hierarchical MDL-Based Clustering. | Shigeru Maya, Kai Morino, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi |
| 2014 | EDM | Extracting Latent Skills from Time Series of Asynchronous and Incomplete Examinations. | Shinichi Oeda, Yu Ito, Kenji Yamanishi |
| 2014 | ICDM | Data Fusion Using Restricted Boltzmann Machines. | Yoshiki Sakai, Kenji Yamanishi |
| 2013 | EDM | Extracting Time-evolving Latent Skills from Examination Time Series. | Shinichi Oeda, Kenji Yamanishi |
| 2013 | ICDM | Quantitative Prediction of Glaucomatous Visual Field Loss from Few Measurements. | Zenghan Liang, Ryota Tomioka, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi |
| 2013 | ICDM | Graph Partitioning Change Detection Using Tree-Based Clustering. | Sho-Ichi Sato, Kenji Yamanishi |
| 2012 | ICDM | Sequential Network Change Detection with Its Applications to Ad Impact Relation Analysis. | Yu Hayashi, Kenji Yamanishi |
| 2012 | ITW | An MDL-based change-detection algorithm with its applications to learning piecewise stationary memoryless sources. | Hiroki Kanazawa, Kenji Yamanishi |
| 2012 | ITW | Comparison of dynamic model selection with infinite HMM for statistical model change detection. | Eiichi Sakurai, Kenji Yamanishi |
| 2012 | KDD | Detecting changes of clustering structures using normalized maximum likelihood coding. | So Hirai, Kenji Yamanishi |
| 2011 | ICDM | Discovering Emerging Topics in Social Streams via Link Anomaly Detection. | Toshimitsu Takahashi, Ryota Tomioka, Kenji Yamanishi |
| 2011 | ISIT | Efficient computation of normalized maximum likelihood coding for Gaussian mixtures with its applications to optimal clustering. | So Hirai, Kenji Yamanishi |
| 2011 | PAKDD | Real-Time Change-Point Detection Using Sequentially Discounting Normalized Maximum Likelihood Coding. | Yasuhiro Urabe, Kenji Yamanishi, Ryota Tomioka, Hiroki Iwai |
| 2009 | KDD | Network anomaly detection based on Eigen equation compression. | Shunsuke Hirose, Kenji Yamanishi, Takayuki Nakata, Ryohei Fujimaki |
| 2008 | SDM | Latent Variable Mining with Its Applications to Anomalous Behavior Detection. | Shunsuke Hirose, Kenji Yamanishi |
| 2005 | KDD | Dynamic syslog mining for network failure monitoring. | Kenji Yamanishi, Yuko Maruyama |
| 2004 | ITW | Dynamic model selection with its applications to computer security. | Yuko Maruyama, Kenji Yamanishi |
| 2004 | KDD | Tracking dynamics of topic trends using a finite mixture model. | Satoshi Morinaga, Kenji Yamanishi |
| 2003 | KDD | Distributed cooperative mining for information consortia. | Satoshi Morinaga, Kenji Yamanishi, Jun'ichi Takeuchi |
| 2002 | KDD | Mining product reputations on the Web. | Satoshi Morinaga, Kenji Yamanishi, Kenji Tateishi, Toshikazu Fukushima |
| 2002 | KDD | A unifying framework for detecting outliers and change points from non-stationary time series data. | Kenji Yamanishi, Jun'ichi Takeuchi |
| 2001 | KDD | Mining from open answers in questionnaire data. | Hang Li, Kenji Yamanishi |
| 2001 | KDD | Discovering outlier filtering rules from unlabeled data: combining a supervised learner with an unsupervised learner. | Kenji Yamanishi, Jun'ichi Takeuchi |
| 2000 | EMNLP | Topic Analysis Using a Finite Mixture Model. | Hang Li, Kenji Yamanishi |
| 2000 | KDD | On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. | Kenji Yamanishi, Jun'ichi Takeuchi, Graham J. Williams, Peter Milne |
| 1999 | ALT | Extended Stochastic Complexity and Minimax Relative Loss Analysis. | Kenji Yamanishi |
| 1999 | CIKM | Text Classification Using ESC-based Stochastic Decision Lists. | Hang Li, Kenji Yamanishi |
| 1998 | COLT | Minimax Relative Loss Analysis for Sequential Prediction Algorithms Using Parametric Hypotheses. | Kenji Yamanishi |
| 1997 | ACL | Document Classification Using a Finite Mixture Model. | Hang Li, Kenji Yamanishi |
| 1997 | COLT | Distributed Cooperative Bayesian Learning Strategies. | Kenji Yamanishi |
| 1996 | COLT | A Randomized Approximation of the MDL for Stochastic Models with Hidden Variables. | Kenji Yamanishi |
| 1995 | COLT | Randomized Approximate Aggregating Strategies and Their Applications to Prediction and Discrimination. | Kenji Yamanishi |
| 1994 | COLT | The Minimum | Kenji Yamanishi |
| 1993 | COLT | On Polynomial-Time Probably almost Discriminative Learnability. | Kenji Yamanishi |
| 1992 | ALT | Protein Secondary Structure Prediction Based on Stochastic-Rule Learning. | Hiroshi Mamitsuka, Kenji Yamanishi |
| 1992 | COLT | Probably Almost Discriminative Learning. | Kenji Yamanishi |
| 1991 | ALT | Learning non-parametric densities by finite-dimensional parametric hypotheses. | Kenji Yamanishi |
| 1991 | COLT | A Loss Bound Model for On-Line Stochastic Prediction Strategies. | Kenji Yamanishi |
| 1991 | ICML | Learning Stochastic Motifs from Genetic Sequences. | Kenji Yamanishi, Akihiko Konagaya |
| 1990 | COLT | A Learning Criterion for Stochastic Rules. | Kenji Yamanishi |