A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems. - Yoshinobu Kawahara; Takehisa Yairi; Kazuo Machida (NIPS, 2006)

Change-Point Detection in Time-Series Data Based on Subspace Identification. - Yoshinobu Kawahara; Takehisa Yairi; Kazuo Machida (ICDM, 2007)

Submodularity Cuts and Applications. - Yoshinobu Kawahara; Kiyohito Nagano; Koji Tsuda; Jeff A. Bilmes (NIPS, 2009)

Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation. - Yoshinobu Kawahara; Masashi Sugiyama (SDM, 2009)

A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model. - Shohei Shimizu; Aapo Hyvärinen; Yoshinobu Kawahara (UAI, 2009)

Stationary Subspace Analysis as a Generalized Eigenvalue Problem. - Satoshi Hara 001; Yoshinobu Kawahara; Takashi Washio; Paul von Bünau (ICONIP (1), 2010)

Learning Non-linear Dynamical Systems by Alignment of Local Linear Models. - Masao Joko; Yoshinobu Kawahara; Takehisa Yairi (ICPR, 2010)

An experimental comparison of linear non-Gaussian causal discovery methods and their variants. - Yasuhiro Sogawa; Shohei Shimizu; Yoshinobu Kawahara; Takashi Washio (IJCNN, 2010)

Minimum Average Cost Clustering. - Kiyohito Nagano; Yoshinobu Kawahara; Satoru Iwata 001 (NIPS, 2010)

GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables - Yoshinobu Kawahara; Kenneth Bollen; Shohei Shimizu; Takashi Washio (CoRR, 2010)

Size-constrained Submodular Minimization through Minimum Norm Base. - Kiyohito Nagano; Yoshinobu Kawahara; Kazuyuki Aihara (ICML, 2011)

Prismatic Algorithm for Discrete D.C. Programming Problem. - Yoshinobu Kawahara; Takashi Washio (NIPS, 2011)

Discovering causal structures in binary exclusive-or skew acyclic models. - Takanori Inazumi; Takashi Washio; Shohei Shimizu; Joe Suzuki; Akihiro Yamamoto; Yoshinobu Kawahara (UAI, 2011)

Prismatic Algorithm for Discrete D.C. Programming Problems - Yoshinobu Kawahara; Takashi Washio (CoRR, 2011)

Analyzing relationships among ARMA processes based on non-Gaussianity of external influences. - Yoshinobu Kawahara; Shohei Shimizu; Takashi Washio (Neurocomputing, 2011)

DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model. - Shohei Shimizu; Takanori Inazumi; Yasuhiro Sogawa; Aapo Hyvärinen; Yoshinobu Kawahara; Takashi Washio; Patrik O. Hoyer; Kenneth Bollen (J. Mach. Learn. Res., 2011)

Submodular fractional programming for balanced clustering. - Yoshinobu Kawahara; Kiyohito Nagano; Yoshio Okamoto (Pattern Recognit. Lett., 2011)

Robust Active Learning for Linear Regression via Density Power Divergence. - Yasuhiro Sogawa; Tsuyoshi Ueno; Yoshinobu Kawahara; Takashi Washio (ICONIP (3), 2012)

Weighted Likelihood Policy Search with Model Selection. - Tsuyoshi Ueno; Kohei Hayashi; Takashi Washio; Yoshinobu Kawahara (NIPS, 2012)

Separation of stationary and non-stationary sources with a generalized eigenvalue problem. - Satoshi Hara 001; Yoshinobu Kawahara; Takashi Washio; Paul von Bünau; Terumasa Tokunaga; Kiyohumi Yumoto (Neural Networks, 2012)

Sequential change-point detection based on direct density-ratio estimation. - Yoshinobu Kawahara; Masashi Sugiyama (Stat. Anal. Data Min., 2012)

A Novel Structural AR Modeling Approach for a Continuous Time Linear Markov System. - Marina Demeshko; Takashi Washio; Yoshinobu Kawahara (ICDM Workshops, 2013)

Arrangement of Low-Dimensional Parallel Coordinate Plots for High-Dimensional Data Visualization. - Haruka Suematsu; Yunzhu Zheng; Takayuki Itoh; Ryohei Fujimaki; Satoshi Morinaga; Yoshinobu Kawahara (IV, 2013)

Structured Convex Optimization under Submodular Constraints. - Kiyohito Nagano; Yoshinobu Kawahara (UAI, 2013)

Efficient network-guided multi-locus association mapping with graph cuts. - Chloé-Agathe Azencott; Dominik G. Grimm; Mahito Sugiyama; Yoshinobu Kawahara; Karsten M. Borgwardt (Bioinform., 2013)

Simultaneous pursuit of out-of-sample performance and sparsity in index tracking portfolios. - Akiko Takeda; Mahesan Niranjan; Jun-ya Gotoh; Yoshinobu Kawahara (Comput. Manag. Sci., 2013)

Active learning for noisy oracle via density power divergence. - Yasuhiro Sogawa; Tsuyoshi Ueno; Yoshinobu Kawahara; Takashi Washio (Neural Networks, 2013)

Efficient Generalized Fused Lasso and its Application to the Diagnosis of Alzheimer's Disease. - Bo Xin; Yoshinobu Kawahara; Yizhou Wang 001; Wen Gao 001 (AAAI, 2014)

Workshop on Graph-Based Algorithms for Big Data and Its Applications (GABA2014). - Yoshinobu Kawahara; Tetsuji Kuboyama; Hiroshi Sakamoto (JSAI-isAI Workshops, 2014)

Multi-Task Feature Selection on Multiple Networks via Maximum Flows. - Mahito Sugiyama; Chloé-Agathe Azencott; Dominik G. Grimm; Yoshinobu Kawahara; Karsten M. Borgwardt (SDM, 2014)

Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM. - Takanori Inazumi; Takashi Washio; Shohei Shimizu; Joe Suzuki; Akihiro Yamamoto; Yoshinobu Kawahara (CoRR, 2014)

On Approximate Non-submodular Minimization via Tree-Structured Supermodularity. - Yoshinobu Kawahara; Rishabh K. Iyer; Jeff A. Bilmes (AISTATS, 2015)

Skill grouping method: Mining and clustering skill differences from body movement BigData. - Shinichi Yamagiwa; Yoshinobu Kawahara; Noriyuki Tabuchi; Yoshinobu Watanabe; Takeshi Naruo (IEEE BigData, 2015)

Higher Order Fused Regularization for Supervised Learning with Grouped Parameters. - Koh Takeuchi 001; Yoshinobu Kawahara; Tomoharu Iwata (ECML/PKDD (1), 2015)

Parametric Maxflows for Structured Sparse Learning with Convex Relaxations of Submodular Functions. - Yoshinobu Kawahara; Yutaro Yamaguchi 001 (CoRR, 2015)

Scatterplot layout for high-dimensional data visualization. - Yunzhu Zheng; Haruka Suematsu; Takayuki Itoh; Ryohei Fujimaki; Satoshi Morinaga; Yoshinobu Kawahara (J. Vis., 2015)

Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis. - Yoshinobu Kawahara (NIPS, 2016)

A Novel Continuous and Structural VAR Modeling Approach and Its Application to Reactor Noise Analysis. - Marina Demeshko; Takashi Washio; Yoshinobu Kawahara; Yuriy Pepyolyshev (ACM Trans. Intell. Syst. Technol., 2016)

Efficient Generalized Fused Lasso and Its Applications. - Bo Xin; Yoshinobu Kawahara; Yizhou Wang 001; Lingjing Hu; Wen Gao 001 (ACM Trans. Intell. Syst. Technol., 2016)

Sparse nonnegative dynamic mode decomposition. - Naoya Takeishi; Yoshinobu Kawahara; Takehisa Yairi (ICIP, 2017)

Bayesian Dynamic Mode Decomposition. - Naoya Takeishi; Yoshinobu Kawahara; Yasuo Tabei; Takehisa Yairi (IJCAI, 2017)

Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition. - Naoya Takeishi; Yoshinobu Kawahara; Takehisa Yairi (NIPS, 2017)

Koopman Spectral Kernels for Comparing Complex Dynamics: Application to Multiagent Sport Plays. - Keisuke Fujii 001; Yuki Inaba; Yoshinobu Kawahara (ECML/PKDD (3), 2017)

Structurally Regularized Non-negative Tensor Factorization for Spatio-Temporal Pattern Discoveries. - Koh Takeuchi 001; Yoshinobu Kawahara; Tomoharu Iwata (ECML/PKDD (1), 2017)

Representative Selection with Structured Sparsity. - Hongxing Wang 001; Yoshinobu Kawahara; Chaoqun Weng; Junsong Yuan 001 (Pattern Recognit., 2017)

Factorially Switching Dynamic Mode Decomposition for Koopman Analysis of Time-Variant Systems. - Naoya Takeishi; Takehisa Yairi; Yoshinobu Kawahara (CDC, 2018)

Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators. - Isao Ishikawa; Keisuke Fujii 001; Masahiro Ikeda; Yuka Hashimoto; Yoshinobu Kawahara (NeurIPS, 2018)

Metric on Nonlinear Dynamical Systems with Koopman Operators. - Isao Ishikawa; Keisuke Fujii 001; Masahiro Ikeda; Yuka Hashimoto; Yoshinobu Kawahara (CoRR, 2018)

An efficient branch-and-bound algorithm for submodular function maximization. - Naoya Uematsu; Shunji Umetani; Yoshinobu Kawahara (CoRR, 2018)

Prediction and classification in equation-free collective motion dynamics. - Keisuke Fujii 001; Takeshi Kawasaki; Yuki Inaba; Yoshinobu Kawahara (PLoS Comput. Biol., 2018)

Active Change-Point Detection. - Shogo Hayashi; Yoshinobu Kawahara; Hisashi Kashima (ACML, 2019)

Learning with Coherence Patterns in Multivariate Time-series Data via Dynamic Mode Decomposition. - Takehito Bito; Masashi Hiraoka; Yoshinobu Kawahara (IJCNN, 2019)

An Efficient Branch-and-Cut Algorithm for Approximately Submodular Function Maximization. - Naoya Uematsu; Shunji Umetani; Yoshinobu Kawahara (SMC, 2019)

Variational Inference of Penalized Regression with Submodular Functions. - Koh Takeuchi 001; Yuichi Yoshida; Yoshinobu Kawahara (UAI, 2019)

Regularizing Generative Models Using Knowledge of Feature Dependence. - Naoya Takeishi; Yoshinobu Kawahara (CoRR, 2019)

Physically-interpretable classification of network dynamics for complex collective motions. - Keisuke Fujii 001; Naoya Takeishi; Motokazu Hojo; Yuki Inaba; Yoshinobu Kawahara (CoRR, 2019)

Metric on random dynamical systems with vector-valued reproducing kernel Hilbert spaces. - Isao Ishikawa; Akinori Tanaka; Masahiro Ikeda; Yoshinobu Kawahara (CoRR, 2019)

Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables. - Keisuke Fujii 001; Yoshinobu Kawahara (Neural Networks, 2019)

Supervised dynamic mode decomposition via multitask learning. - Keisuke Fujii 001; Yoshinobu Kawahara (Pattern Recognit. Lett., 2019)

Learning Multiple Nonlinear Dynamical Systems with Side Information. - Naoya Takeishi; Yoshinobu Kawahara (CDC, 2020)

Knowledge-Based Regularization in Generative Modeling. - Naoya Takeishi; Yoshinobu Kawahara (IJCAI, 2020)

Analysis via Orthonormal Systems in Reproducing Kernel Hilbert C*-Modules and Applications. - Yuka Hashimoto; Isao Ishikawa; Masahiro Ikeda; Fuyuta Komura; Takeshi Katsura; Yoshinobu Kawahara (CoRR, 2020)

On Anomaly Interpretation via Shapley Values. - Naoya Takeishi; Yoshinobu Kawahara (CoRR, 2020)

Policy learning with partial observation and mechanical constraints for multi-person modeling. - Keisuke Fujii 001; Naoya Takeishi; Yoshinobu Kawahara; Kazuya Takeda (CoRR, 2020)

Kernel Mean Embeddings of Von Neumann-Algebra-Valued Measures. - Yuka Hashimoto; Isao Ishikawa; Masahiro Ikeda; Fuyuta Komura; Yoshinobu Kawahara (CoRR, 2020)

Neural Dynamic Mode Decomposition for End-to-End Modeling of Nonlinear Dynamics. - Tomoharu Iwata; Yoshinobu Kawahara (CoRR, 2020)

Dynamic mode decomposition via dictionary learning for foreground modeling in videos. - Israr Ul Haq; Keisuke Fujii 001; Yoshinobu Kawahara (Comput. Vis. Image Underst., 2020)

Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise. - Yuka Hashimoto; Isao Ishikawa; Masahiro Ikeda; Yoichi Matsuo; Yoshinobu Kawahara (J. Mach. Learn. Res., 2020)

Prediction of Compound Bioactivities Using Heat-Diffusion Equation. - Tadashi Hidaka; Keiko Imamura; Takeshi Hioki; Terufumi Takagi; Yoshikazu Giga; Mi-Ho Giga; Yoshiteru Nishimura; Yoshinobu Kawahara; Satoru Hayashi; Takeshi Niki; Makoto Fushimi; Haruhisa Inoue (Patterns, 2020)

Learning Dynamics Models with Stable Invariant Sets. - Naoya Takeishi; Yoshinobu Kawahara (AAAI, 2021)

Controlling Nonlinear Dynamical Systems with Linear Quadratic Regulator-based Policy Networks in Koopman space. - Tomoharu Iwata; Yoshinobu Kawahara (CDC, 2021)

Learning interaction rules from multi-animal trajectories via augmented behavioral models. - Keisuke Fujii 001; Naoya Takeishi; Kazushi Tsutsui; Emyo Fujioka; Nozomi Nishiumi; Ryoya Tanaka; Mika Fukushiro; Kaoru Ide; Hiroyoshi Kohno; Ken Yoda; Susumu Takahashi; Shizuko Hiryu; et al. (NeurIPS, 2021)

Meta-Learning for Koopman Spectral Analysis with Short Time-series. - Tomoharu Iwata; Yoshinobu Kawahara (CoRR, 2021)

Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections. - Naoya Takeishi; Keisuke Fujii 001; Koh Takeuchi 001; Yoshinobu Kawahara (CoRR, 2021)

A Quadratic Actor Network for Model-Free Reinforcement Learning. - Matthias Weissenbacher; Yoshinobu Kawahara (CoRR, 2021)

Koopman Spectrum Nonlinear Regulator and Provably Efficient Online Learning. - Motoya Ohnishi; Isao Ishikawa; Kendall Lowrey; Masahiro Ikeda; Sham M. Kakade; Yoshinobu Kawahara (CoRR, 2021)

Reproducing kernel Hilbert C*-module and kernel mean embeddings. - Yuka Hashimoto; Isao Ishikawa; Masahiro Ikeda; Fuyuta Komura; Takeshi Katsura; Yoshinobu Kawahara (J. Mach. Learn. Res., 2021)

Estimating counterfactual treatment outcomes over time in multi-vehicle simulation. - Keisuke Fujii 001; Koh Takeuchi 001; Atsushi Kuribayashi; Naoya Takeishi; Yoshinobu Kawahara; Kazuya Takeda (SIGSPATIAL/GIS, 2022)

Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics. - Matthias Weissenbacher; Samarth Sinha; Animesh Garg; Yoshinobu Kawahara (ICML, 2022)

Estimating counterfactual treatment outcomes over time in complex multi-agent scenarios. - Keisuke Fujii 001; Koh Takeuchi 001; Atsushi Kuribayashi; Naoya Takeishi; Yoshinobu Kawahara; Kazuya Takeda (CoRR, 2022)

Data-driven End-to-end Learning of Pole Placement Control for Nonlinear Dynamics via Koopman Invariant Subspaces. - Tomoharu Iwata; Yoshinobu Kawahara (CoRR, 2022)

Modeling Nonlinear Dynamics in Continuous Time with Inductive Biases on Decay Rates and/or Frequencies. - Tomoharu Iwata; Yoshinobu Kawahara (CoRR, 2022)

Dynamic mode decomposition via convolutional autoencoders for dynamics modeling in videos. - Israr Ul Haq; Tomoharu Iwata; Yoshinobu Kawahara (Comput. Vis. Image Underst., 2022)

Predicting behavior through dynamic modes in resting-state fMRI data. - Shigeyuki Ikeda; Koki Kawano; Soichi Watanabe; Okito Yamashita; Yoshinobu Kawahara (NeuroImage, 2022)

Discriminant Dynamic Mode Decomposition for Labeled Spatiotemporal Data Collections. - Naoya Takeishi; Keisuke Fujii 001; Koh Takeuchi 001; Yoshinobu Kawahara (SIAM J. Appl. Dyn. Syst., 2022)

Many-body Approximation for Non-negative Tensors. - Kazu Ghalamkari; Mahito Sugiyama; Yoshinobu Kawahara (NeurIPS, 2023)

Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition. - Ryohei Fukuma; Kei Majima; Yoshinobu Kawahara; Okito Yamashita; Yoshiyuki Shiraishi; Haruhiko Kishima; Takufumi Yanagisawa (CoRR, 2023)

A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores. - Naoya Takeishi; Yoshinobu Kawahara (Trans. Mach. Learn. Res., 2023)

Stable invariant models via Koopman spectra. - Takuya Konishi; Yoshinobu Kawahara (Neural Networks, 2023)

euMMD: efficiently computing the MMD two-sample test statistic for univariate data. - Dean A. Bodenham; Yoshinobu Kawahara (Stat. Comput., 2023)

Adaptive Action Supervision in Reinforcement Learning from Real-World Multi-Agent Demonstrations. - Keisuke Fujii 001; Kazushi Tsutsui; Atom Scott; Hiroshi Nakahara; Naoya Takeishi; Yoshinobu Kawahara (ICAART (2), 2024)

SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning. - Matthias Weissenbacher; Rishabh Agarwal; Yoshinobu Kawahara (ICML, 2024)

Glocal Hypergradient Estimation with Koopman Operator. - Ryuichiro Hataya; Yoshinobu Kawahara (CoRR, 2024)

Koopman operators with intrinsic observables in rigged reproducing kernel Hilbert spaces. - Isao Ishikawa; Yuka Hashimoto; Masahiro Ikeda; Yoshinobu Kawahara (CoRR, 2024)

The Disappearance of Timestep Embedding in Modern Time-Dependent Neural Networks. - Bum Jun Kim; Yoshinobu Kawahara; Sang Woo Kim (CoRR, 2024)

Change-Point Detection in Industrial Data Streams based on Online Dynamic Mode Decomposition with Control. - Marek Wadinger; Michal Kvasnica; Yoshinobu Kawahara (CoRR, 2024)

Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling. - Keisuke Fujii 001; Naoya Takeishi; Yoshinobu Kawahara; Kazuya Takeda (Neural Networks, 2024)

Koopman Spectrum Nonlinear Regulators and Efficient Online Learning. - Motoya Ohnishi; Isao Ishikawa; Kendall Lowrey; Masahiro Ikeda; Sham M. Kakade; Yoshinobu Kawahara (Trans. Mach. Learn. Res., 2024)

Self-supervised Color Generalization in Reinforcement Learning. - Matthias Weissenbacher; Evangelos Routis; Yoshinobu Kawahara (Trans. Mach. Learn. Res., 2024)

MANet: Mixed Attention Network for Visual Explanation. - Jingjing Bai; Yoshinobu Kawahara (New Gener. Comput., 2024)

Learning Stochastic Nonlinear Dynamics with Embedded Latent Transfer Operators. - Naichang Ke; Ryogo Tanaka; Yoshinobu Kawahara (AISTATS, 2025)

Disappearance of Timestep Embedding: A Case Study on Neural ODE and Diffusion Models. - Bum Jun Kim; Yoshinobu Kawahara; Sang Woo Kim (Trans. Mach. Learn. Res., 2025)

Estimating Counterfactual Treatment Outcomes Over Time in Complex Multiagent Scenarios. - Keisuke Fujii 001; Koh Takeuchi 001; Atsushi Kuribayashi; Naoya Takeishi; Yoshinobu Kawahara; Kazuya Takeda (IEEE Trans. Neural Networks Learn. Syst., 2025)

Explorative Curriculum Learning for Strongly Correlated Electron Systems. - Kimihiro Yamazaki; Takuya Konishi; Yoshinobu Kawahara (CoRR, 2025)

Wavy Transformer. - Satoshi Noguchi; Yoshinobu Kawahara (CoRR, 2025)