Yoshinobu Kawahara

127 papers A* 11A 8B 5C 2Journal 80Unranked 21

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	An Operator Analysis on Stochastic Differential Equation (SDE)-Based Diffusion Generative Models. Entropy Yunpei Wu, Yoshinobu Kawahara
2026	J	`jnl`	Dualformer: Time-Frequency Dual Domain Learning for Long-term Time Series Forecasting. CoRR Jingjing Bai, Yoshinobu Kawahara
2026	J	`jnl`	Infinitely deep Bayesian neural network with signature transform. Neurocomputing Xiaoyu Yang, Peiyi Qiu, Yoshinobu Kawahara
2026	J	`jnl`	Physics-inspired transformer quantum states via latent imaginary-time evolution. CoRR Kimihiro Yamazaki, Itsushi Sakata, Takuya Konishi, Yoshinobu Kawahara
2026	J	`jnl`	Predictive Fair Representation Learning with Variational Autoencoders. New Gener. Comput. Tatsuya Yamada, Takuya Konishi, Yoshinobu Kawahara
2025	J	`jnl`	Data-driven simulator of multi-animal behavior with unknown dynamics via offline and online reinforcement learning. CoRR Keisuke Fujii, Kazushi Tsutsui, Yu Teshima, Makoto Itoh, Naoya Takeishi, Nozomi Nishiumi, Ryoya Tanaka, Shunsuke Shigaki, Yoshinobu Kawahara
2025	J	`jnl`	Disappearance of Timestep Embedding: A Case Study on Neural ODE and Diffusion Models. Trans. Mach. Learn. Res. Bum Jun Kim, Yoshinobu Kawahara, Sang Woo Kim
2025	J	`jnl`	Estimating Counterfactual Treatment Outcomes Over Time in Complex Multiagent Scenarios. IEEE Trans. Neural Networks Learn. Syst. Keisuke Fujii, Koh Takeuchi, Atsushi Kuribayashi, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda
2025	J	`jnl`	Explorative Curriculum Learning for Strongly Correlated Electron Systems. CoRR Kimihiro Yamazaki, Takuya Konishi, Yoshinobu Kawahara
2025	A	`conf`	Learning Stochastic Nonlinear Dynamics with Embedded Latent Transfer Operators. AISTATS Naichang Ke, Ryogo Tanaka, Yoshinobu Kawahara
2025	J	`jnl`	Learning Stochastic Nonlinear Dynamics with Embedded Latent Transfer Operators. CoRR Naichang Ke, Ryogo Tanaka, Yoshinobu Kawahara
2025	J	`jnl`	Wavy Transformer. CoRR Satoshi Noguchi, Yoshinobu Kawahara
2024	—	`conf`	Adaptive Action Supervision in Reinforcement Learning from Real-World Multi-Agent Demonstrations. ICAART (2) Keisuke Fujii, Kazushi Tsutsui, Atom Scott, Hiroshi Nakahara, Naoya Takeishi, Yoshinobu Kawahara
2024	J	`jnl`	Change-Point Detection in Industrial Data Streams based on Online Dynamic Mode Decomposition with Control. CoRR Marek Wadinger, Michal Kvasnica, Yoshinobu Kawahara
2024	J	`jnl`	Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling. Neural Networks Keisuke Fujii, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda
2024	J	`jnl`	Glocal Hypergradient Estimation with Koopman Operator. CoRR Ryuichiro Hataya, Yoshinobu Kawahara
2024	J	`jnl`	Koopman Spectrum Nonlinear Regulators and Efficient Online Learning. Trans. Mach. Learn. Res. Motoya Ohnishi, Isao Ishikawa, Kendall Lowrey, Masahiro Ikeda, Sham M. Kakade, Yoshinobu Kawahara
2024	J	`jnl`	Koopman operators with intrinsic observables in rigged reproducing kernel Hilbert spaces. CoRR Isao Ishikawa, Yuka Hashimoto, Masahiro Ikeda, Yoshinobu Kawahara
2024	J	`jnl`	MANet: Mixed Attention Network for Visual Explanation. New Gener. Comput. Jingjing Bai, Yoshinobu Kawahara
2024	J	`jnl`	Self-supervised Color Generalization in Reinforcement Learning. Trans. Mach. Learn. Res. Matthias Weissenbacher, Evangelos Routis, Yoshinobu Kawahara
2024	J	`jnl`	SiT: Symmetry-Invariant Transformers for Generalisation in Reinforcement Learning. CoRR Matthias Weissenbacher, Rishabh Agarwal, Yoshinobu Kawahara
2024	A*	`conf`	SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning. ICML Matthias Weissenbacher, Rishabh Agarwal, Yoshinobu Kawahara
2024	J	`jnl`	The Disappearance of Timestep Embedding in Modern Time-Dependent Neural Networks. CoRR Bum Jun Kim, Yoshinobu Kawahara, Sang Woo Kim
2023	J	`jnl`	A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores. Trans. Mach. Learn. Res. Naoya Takeishi, Yoshinobu Kawahara
2023	J	`jnl`	Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations. CoRR Keisuke Fujii, Kazushi Tsutsui, Atom Scott, Hiroshi Nakahara, Naoya Takeishi, Yoshinobu Kawahara
2023	J	`jnl`	Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition. CoRR Ryohei Fukuma, Kei Majima, Yoshinobu Kawahara, Okito Yamashita, Yoshiyuki Shiraishi, Haruhiko Kishima, Takufumi Yanagisawa
2023	A*	`conf`	Many-body Approximation for Non-negative Tensors. NeurIPS Kazu Ghalamkari, Mahito Sugiyama, Yoshinobu Kawahara
2023	J	`jnl`	Stable invariant models via Koopman spectra. Neural Networks Takuya Konishi, Yoshinobu Kawahara
2023	J	`jnl`	euMMD: efficiently computing the MMD two-sample test statistic for univariate data. Stat. Comput. Dean A. Bodenham, Yoshinobu Kawahara
2022	J	`jnl`	Data-driven End-to-end Learning of Pole Placement Control for Nonlinear Dynamics via Koopman Invariant Subspaces. CoRR Tomoharu Iwata, Yoshinobu Kawahara
2022	J	`jnl`	Discriminant Dynamic Mode Decomposition for Labeled Spatiotemporal Data Collections. SIAM J. Appl. Dyn. Syst. Naoya Takeishi, Keisuke Fujii, Koh Takeuchi, Yoshinobu Kawahara
2022	J	`jnl`	Dynamic mode decomposition via convolutional autoencoders for dynamics modeling in videos. Comput. Vis. Image Underst. Israr Ul Haq, Tomoharu Iwata, Yoshinobu Kawahara
2022	J	`jnl`	Estimating counterfactual treatment outcomes over time in complex multi-agent scenarios. CoRR Keisuke Fujii, Koh Takeuchi, Atsushi Kuribayashi, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda
2022	—	`conf`	Estimating counterfactual treatment outcomes over time in multi-vehicle simulation. SIGSPATIAL/GIS Keisuke Fujii, Koh Takeuchi, Atsushi Kuribayashi, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda
2022	A*	`conf`	Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics. ICML Matthias Weissenbacher, Samarth Sinha, Animesh Garg, Yoshinobu Kawahara
2022	J	`jnl`	Modeling Nonlinear Dynamics in Continuous Time with Inductive Biases on Decay Rates and/or Frequencies. CoRR Tomoharu Iwata, Yoshinobu Kawahara
2022	J	`jnl`	Predicting behavior through dynamic modes in resting-state fMRI data. NeuroImage Shigeyuki Ikeda, Koki Kawano, Soichi Watanabe, Okito Yamashita, Yoshinobu Kawahara
2022	J	`jnl`	Stable Invariant Models via Koopman Spectra. CoRR Takuya Konishi, Yoshinobu Kawahara
2021	J	`jnl`	A Quadratic Actor Network for Model-Free Reinforcement Learning. CoRR Matthias Weissenbacher, Yoshinobu Kawahara
2021	—	`conf`	Controlling Nonlinear Dynamical Systems with Linear Quadratic Regulator-based Policy Networks in Koopman space. CDC Tomoharu Iwata, Yoshinobu Kawahara
2021	J	`jnl`	Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections. CoRR Naoya Takeishi, Keisuke Fujii, Koh Takeuchi, Yoshinobu Kawahara
2021	J	`jnl`	Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics. CoRR Matthias Weissenbacher, Samarth Sinha, Animesh Garg, Yoshinobu Kawahara
2021	J	`jnl`	Koopman Spectrum Nonlinear Regulator and Provably Efficient Online Learning. CoRR Motoya Ohnishi, Isao Ishikawa, Kendall Lowrey, Masahiro Ikeda, Sham M. Kakade, Yoshinobu Kawahara
2021	A*	`conf`	Learning Dynamics Models with Stable Invariant Sets. AAAI Naoya Takeishi, Yoshinobu Kawahara
2021	A*	`conf`	Learning interaction rules from multi-animal trajectories via augmented behavioral models. NeurIPS Keisuke Fujii, Naoya Takeishi, Kazushi Tsutsui, Emyo Fujioka, Nozomi Nishiumi, Ryoya Tanaka, Mika Fukushiro, Kaoru Ide, Hiroyoshi Kohno, Ken Yoda, Susumu Takahashi, Shizuko Hiryu, Yoshinobu Kawahara
2021	J	`jnl`	Learning interaction rules from multi-animal trajectories via augmented behavioral models. CoRR Keisuke Fujii, Naoya Takeishi, Kazushi Tsutsui, Emyo Fujioka, Nozomi Nishiumi, Ryoya Tanaka, Mika Fukushiro, Kaoru Ide, Hiroyoshi Kohno, Ken Yoda, Susumu Takahashi, Shizuko Hiryu, Yoshinobu Kawahara
2021	J	`jnl`	Meta-Learning for Koopman Spectral Analysis with Short Time-series. CoRR Tomoharu Iwata, Yoshinobu Kawahara
2021	J	`jnl`	Reproducing kernel Hilbert C*-module and kernel mean embeddings. CoRR Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Takeshi Katsura, Yoshinobu Kawahara
2021	J	`jnl`	Reproducing kernel Hilbert C*-module and kernel mean embeddings. J. Mach. Learn. Res. Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Takeshi Katsura, Yoshinobu Kawahara
2020	J	`jnl`	Analysis via Orthonormal Systems in Reproducing Kernel Hilbert C CoRR Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Takeshi Katsura, Yoshinobu Kawahara
2020	—	`conf`	Dynamic Mode Decomposition via Dictionary Learning for Foreground Modeling in Videos. VISIGRAPP (5: VISAPP) Israr Ul Haq, Keisuke Fujii, Yoshinobu Kawahara
2020	J	`jnl`	Dynamic mode decomposition via dictionary learning for foreground modeling in videos. Comput. Vis. Image Underst. Israr Ul Haq, Keisuke Fujii, Yoshinobu Kawahara
2020	J	`jnl`	Kernel Mean Embeddings of Von Neumann-Algebra-Valued Measures. CoRR Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Yoshinobu Kawahara
2020	A*	`conf`	Knowledge-Based Regularization in Generative Modeling. IJCAI Naoya Takeishi, Yoshinobu Kawahara
2020	J	`jnl`	Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise. J. Mach. Learn. Res. Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Yoichi Matsuo, Yoshinobu Kawahara
2020	J	`jnl`	Learning Dynamics Models with Stable Invariant Sets. CoRR Naoya Takeishi, Yoshinobu Kawahara
2020	—	`conf`	Learning Multiple Nonlinear Dynamical Systems with Side Information. CDC Naoya Takeishi, Yoshinobu Kawahara
2020	J	`jnl`	Neural Dynamic Mode Decomposition for End-to-End Modeling of Nonlinear Dynamics. CoRR Tomoharu Iwata, Yoshinobu Kawahara
2020	J	`jnl`	On Anomaly Interpretation via Shapley Values. CoRR Naoya Takeishi, Yoshinobu Kawahara
2020	J	`jnl`	Policy learning with partial observation and mechanical constraints for multi-person modeling. CoRR Keisuke Fujii, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda
2020	J	`jnl`	Prediction of Compound Bioactivities Using Heat-Diffusion Equation. Patterns 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
2019	C	`conf`	Active Change-Point Detection. ACML Shogo Hayashi, Yoshinobu Kawahara, Hisashi Kashima
2019	B	`conf`	An Efficient Branch-and-Cut Algorithm for Approximately Submodular Function Maximization. SMC Naoya Uematsu, Shunji Umetani, Yoshinobu Kawahara
2019	J	`jnl`	An efficient branch-and-cut algorithm for approximately submodular function maximization. CoRR Naoya Uematsu, Shunji Umetani, Yoshinobu Kawahara
2019	J	`jnl`	Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables. Neural Networks Keisuke Fujii, Yoshinobu Kawahara
2019	J	`jnl`	Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise. CoRR Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Yoichi Matsuo, Yoshinobu Kawahara
2019	B	`conf`	Learning with Coherence Patterns in Multivariate Time-series Data via Dynamic Mode Decomposition. IJCNN Takehito Bito, Masashi Hiraoka, Yoshinobu Kawahara
2019	J	`jnl`	Metric on random dynamical systems with vector-valued reproducing kernel Hilbert spaces. CoRR Isao Ishikawa, Akinori Tanaka, Masahiro Ikeda, Yoshinobu Kawahara
2019	J	`jnl`	Physically-interpretable classification of network dynamics for complex collective motions. CoRR Keisuke Fujii, Naoya Takeishi, Motokazu Hojo, Yuki Inaba, Yoshinobu Kawahara
2019	J	`jnl`	Regularizing Generative Models Using Knowledge of Feature Dependence. CoRR Naoya Takeishi, Yoshinobu Kawahara
2019	J	`jnl`	Supervised dynamic mode decomposition via multitask learning. Pattern Recognit. Lett. Keisuke Fujii, Yoshinobu Kawahara
2019	A	`conf`	Variational Inference of Penalized Regression with Submodular Functions. UAI Koh Takeuchi, Yuichi Yoshida, Yoshinobu Kawahara
2018	J	`jnl`	An efficient branch-and-bound algorithm for submodular function maximization. CoRR Naoya Uematsu, Shunji Umetani, Yoshinobu Kawahara
2018	J	`jnl`	Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables. CoRR Keisuke Fujii, Yoshinobu Kawahara
2018	—	`conf`	Factorially Switching Dynamic Mode Decomposition for Koopman Analysis of Time-Variant Systems. CDC Naoya Takeishi, Takehisa Yairi, Yoshinobu Kawahara
2018	J	`jnl`	Metric on Nonlinear Dynamical Systems with Koopman Operators. CoRR Isao Ishikawa, Keisuke Fujii, Masahiro Ikeda, Yuka Hashimoto, Yoshinobu Kawahara
2018	A*	`conf`	Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators. NeurIPS Isao Ishikawa, Keisuke Fujii, Masahiro Ikeda, Yuka Hashimoto, Yoshinobu Kawahara
2018	J	`jnl`	Prediction and classification in equation-free collective motion dynamics. PLoS Comput. Biol. Keisuke Fujii, Takeshi Kawasaki, Yuki Inaba, Yoshinobu Kawahara
2017	A*	`conf`	Bayesian Dynamic Mode Decomposition. IJCAI Naoya Takeishi, Yoshinobu Kawahara, Yasuo Tabei, Takehisa Yairi
2017	—	`conf`	Koopman Spectral Kernels for Comparing Complex Dynamics: Application to Multiagent Sport Plays. ECML/PKDD (3) Keisuke Fujii, Yuki Inaba, Yoshinobu Kawahara
2017	—	`conf`	Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition. NIPS Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi
2017	J	`jnl`	Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition. CoRR Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi
2017	J	`jnl`	Representative Selection with Structured Sparsity. Pattern Recognit. Hongxing Wang, Yoshinobu Kawahara, Chaoqun Weng, Junsong Yuan
2017	B	`conf`	Sparse nonnegative dynamic mode decomposition. ICIP Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi
2017	—	`conf`	Structurally Regularized Non-negative Tensor Factorization for Spatio-Temporal Pattern Discoveries. ECML/PKDD (1) Koh Takeuchi, Yoshinobu Kawahara, Tomoharu Iwata
2016	J	`jnl`	A Novel Continuous and Structural VAR Modeling Approach and Its Application to Reactor Noise Analysis. ACM Trans. Intell. Syst. Technol. Marina Demeshko, Takashi Washio, Yoshinobu Kawahara, Yuriy Pepyolyshev
2016	—	`conf`	Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis. NIPS Yoshinobu Kawahara
2016	J	`jnl`	Efficient Generalized Fused Lasso and Its Applications. ACM Trans. Intell. Syst. Technol. Bo Xin, Yoshinobu Kawahara, Yizhou Wang, Lingjing Hu, Wen Gao
2015	—	`conf`	Higher Order Fused Regularization for Supervised Learning with Grouped Parameters. ECML/PKDD (1) Koh Takeuchi, Yoshinobu Kawahara, Tomoharu Iwata
2015	A	`conf`	On Approximate Non-submodular Minimization via Tree-Structured Supermodularity. AISTATS Yoshinobu Kawahara, Rishabh K. Iyer, Jeff A. Bilmes
2015	J	`jnl`	Parametric Maxflows for Structured Sparse Learning with Convex Relaxations of Submodular Functions. CoRR Yoshinobu Kawahara, Yutaro Yamaguchi
2015	J	`jnl`	Scatterplot layout for high-dimensional data visualization. J. Vis. Yunzhu Zheng, Haruka Suematsu, Takayuki Itoh, Ryohei Fujimaki, Satoshi Morinaga, Yoshinobu Kawahara
2015	—	`conf`	Skill grouping method: Mining and clustering skill differences from body movement BigData. IEEE BigData Shinichi Yamagiwa, Yoshinobu Kawahara, Noriyuki Tabuchi, Yoshinobu Watanabe, Takeshi Naruo
2014	J	`jnl`	A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model. CoRR Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara
2014	J	`jnl`	Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM. CoRR Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara
2014	A*	`conf`	Efficient Generalized Fused Lasso and its Application to the Diagnosis of Alzheimer's Disease. AAAI Bo Xin, Yoshinobu Kawahara, Yizhou Wang, Wen Gao
2014	A	`conf`	Multi-Task Feature Selection on Multiple Networks via Maximum Flows. SDM Mahito Sugiyama, Chloé-Agathe Azencott, Dominik G. Grimm, Yoshinobu Kawahara, Karsten M. Borgwardt
2014	—	`conf`	Workshop on Graph-Based Algorithms for Big Data and Its Applications (GABA2014). JSAI-isAI Workshops Yoshinobu Kawahara, Tetsuji Kuboyama, Hiroshi Sakamoto
2013	—	`conf`	A Novel Structural AR Modeling Approach for a Continuous Time Linear Markov System. ICDM Workshops Marina Demeshko, Takashi Washio, Yoshinobu Kawahara
2013	J	`jnl`	Active learning for noisy oracle via density power divergence. Neural Networks Yasuhiro Sogawa, Tsuyoshi Ueno, Yoshinobu Kawahara, Takashi Washio
2013	C	`conf`	Arrangement of Low-Dimensional Parallel Coordinate Plots for High-Dimensional Data Visualization. IV Haruka Suematsu, Yunzhu Zheng, Takayuki Itoh, Ryohei Fujimaki, Satoshi Morinaga, Yoshinobu Kawahara
2013	J	`jnl`	Efficient network-guided multi-locus association mapping with graph cuts. Bioinform. Chloé-Agathe Azencott, Dominik G. Grimm, Mahito Sugiyama, Yoshinobu Kawahara, Karsten M. Borgwardt
2013	J	`jnl`	Simultaneous pursuit of out-of-sample performance and sparsity in index tracking portfolios. Comput. Manag. Sci. Akiko Takeda, Mahesan Niranjan, Jun-ya Gotoh, Yoshinobu Kawahara
2013	A	`conf`	Structured Convex Optimization under Submodular Constraints. UAI Kiyohito Nagano, Yoshinobu Kawahara
2013	J	`jnl`	Structured Convex Optimization under Submodular Constraints. CoRR Kiyohito Nagano, Yoshinobu Kawahara
2012	J	`jnl`	Discovering causal structures in binary exclusive-or skew acyclic models CoRR Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara
2012	—	`conf`	Robust Active Learning for Linear Regression via Density Power Divergence. ICONIP (3) Yasuhiro Sogawa, Tsuyoshi Ueno, Yoshinobu Kawahara, Takashi Washio
2012	J	`jnl`	Separation of stationary and non-stationary sources with a generalized eigenvalue problem. Neural Networks Satoshi Hara, Yoshinobu Kawahara, Takashi Washio, Paul von Bünau, Terumasa Tokunaga, Kiyohumi Yumoto
2012	J	`jnl`	Sequential change-point detection based on direct density-ratio estimation. Stat. Anal. Data Min. Yoshinobu Kawahara, Masashi Sugiyama
2012	—	`conf`	Weighted Likelihood Policy Search with Model Selection. NIPS Tsuyoshi Ueno, Kohei Hayashi, Takashi Washio, Yoshinobu Kawahara
2011	J	`jnl`	Analyzing relationships among ARMA processes based on non-Gaussianity of external influences. Neurocomputing Yoshinobu Kawahara, Shohei Shimizu, Takashi Washio
2011	J	`jnl`	DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model. J. Mach. Learn. Res. Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvärinen, Yoshinobu Kawahara, Takashi Washio, Patrik O. Hoyer, Kenneth Bollen
2011	A	`conf`	Discovering causal structures in binary exclusive-or skew acyclic models. UAI Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara
2011	—	`conf`	Prismatic Algorithm for Discrete D.C. Programming Problem. NIPS Yoshinobu Kawahara, Takashi Washio
2011	J	`jnl`	Prismatic Algorithm for Discrete D.C. Programming Problems CoRR Yoshinobu Kawahara, Takashi Washio
2011	A*	`conf`	Size-constrained Submodular Minimization through Minimum Norm Base. ICML Kiyohito Nagano, Yoshinobu Kawahara, Kazuyuki Aihara
2011	J	`jnl`	Submodular fractional programming for balanced clustering. Pattern Recognit. Lett. Yoshinobu Kawahara, Kiyohito Nagano, Yoshio Okamoto
2010	B	`conf`	An experimental comparison of linear non-Gaussian causal discovery methods and their variants. IJCNN Yasuhiro Sogawa, Shohei Shimizu, Yoshinobu Kawahara, Takashi Washio
2010	J	`jnl`	GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables CoRR Yoshinobu Kawahara, Kenneth Bollen, Shohei Shimizu, Takashi Washio
2010	B	`conf`	Learning Non-linear Dynamical Systems by Alignment of Local Linear Models. ICPR Masao Joko, Yoshinobu Kawahara, Takehisa Yairi
2010	—	`conf`	Minimum Average Cost Clustering. NIPS Kiyohito Nagano, Yoshinobu Kawahara, Satoru Iwata
2010	—	`conf`	Stationary Subspace Analysis as a Generalized Eigenvalue Problem. ICONIP (1) Satoshi Hara, Yoshinobu Kawahara, Takashi Washio, Paul von Bünau
2009	A	`conf`	A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model. UAI Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara
2009	A	`conf`	Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation. SDM Yoshinobu Kawahara, Masashi Sugiyama
2009	—	`conf`	Submodularity Cuts and Applications. NIPS Yoshinobu Kawahara, Kiyohito Nagano, Koji Tsuda, Jeff A. Bilmes
2007	A*	`conf`	Change-Point Detection in Time-Series Data Based on Subspace Identification. ICDM Yoshinobu Kawahara, Takehisa Yairi, Kazuo Machida
2006	—	`conf`	A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems. NIPS Yoshinobu Kawahara, Takehisa Yairi, Kazuo Machida