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2020 – today
- 2024
[j27]Keisuke Fujii
, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda:
Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling. Neural Networks 171: 40-52 (2024)- [j26]Motoya Ohnishi, Isao Ishikawa, Kendall Lowrey, Masahiro Ikeda, Sham M. Kakade, Yoshinobu Kawahara:
Koopman Spectrum Nonlinear Regulators and Efficient Online Learning. Trans. Mach. Learn. Res. 2024 (2024) - [c46]Keisuke Fujii, Kazushi Tsutsui, Atom Scott, Hiroshi Nakahara, Naoya Takeishi, Yoshinobu Kawahara:
Adaptive Action Supervision in Reinforcement Learning from Real-World Multi-Agent Demonstrations. ICAART (2) 2024: 27-39 - [c45]Matthias Weissenbacher, Rishabh Agarwal, Yoshinobu Kawahara:
SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning. ICML 2024 - [i40]Ryuichiro Hataya, Yoshinobu Kawahara:
Glocal Hypergradient Estimation with Koopman Operator. CoRR abs/2402.02741 (2024) - [i39]Isao Ishikawa, Yuka Hashimoto, Masahiro Ikeda, Yoshinobu Kawahara:
Koopman operators with intrinsic observables in rigged reproducing kernel Hilbert spaces. CoRR abs/2403.02524 (2024) - [i38]Bum Jun Kim, Yoshinobu Kawahara, Sang Woo Kim:
The Disappearance of Timestep Embedding in Modern Time-Dependent Neural Networks. CoRR abs/2405.14126 (2024) - [i37]Matthias Weissenbacher, Rishabh Agarwal, Yoshinobu Kawahara:
SiT: Symmetry-Invariant Transformers for Generalisation in Reinforcement Learning. CoRR abs/2406.15025 (2024) - [i36]Marek Wadinger, Michal Kvasnica, Yoshinobu Kawahara:
Change-Point Detection in Industrial Data Streams based on Online Dynamic Mode Decomposition with Control. CoRR abs/2407.05976 (2024) - 2023
- [j25]Takuya Konishi, Yoshinobu Kawahara:
Stable invariant models via Koopman spectra. Neural Networks 165: 393-405 (2023) - [j24]Dean A. Bodenham, Yoshinobu Kawahara:
euMMD: efficiently computing the MMD two-sample test statistic for univariate data. Stat. Comput. 33(5): 110 (2023) - [j23]Naoya Takeishi, Yoshinobu Kawahara:
A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores. Trans. Mach. Learn. Res. 2023 (2023) - [c44]Kazu Ghalamkari, Mahito Sugiyama, Yoshinobu Kawahara:
Many-body Approximation for Non-negative Tensors. NeurIPS 2023 - [i35]Keisuke Fujii, Kazushi Tsutsui, Atom Scott, Hiroshi Nakahara, Naoya Takeishi, Yoshinobu Kawahara:
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations. CoRR abs/2305.13030 (2023) - [i34]Ryohei Fukuma, Kei Majima, Yoshinobu Kawahara, Okito Yamashita, Yoshiyuki Shiraishi, Haruhiko Kishima, Takufumi Yanagisawa:
Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition. CoRR abs/2311.04225 (2023) - 2022
- [j22]Israr Ul Haq, Tomoharu Iwata, Yoshinobu Kawahara:
Dynamic mode decomposition via convolutional autoencoders for dynamics modeling in videos. Comput. Vis. Image Underst. 216: 103355 (2022) - [j21]Shigeyuki Ikeda, Koki Kawano, Soichi Watanabe, Okito Yamashita, Yoshinobu Kawahara:
Predicting behavior through dynamic modes in resting-state fMRI data. NeuroImage 247: 118801 (2022) - [j20]Naoya Takeishi, Keisuke Fujii, Koh Takeuchi, Yoshinobu Kawahara:
Discriminant Dynamic Mode Decomposition for Labeled Spatiotemporal Data Collections. SIAM J. Appl. Dyn. Syst. 21(2): 1030-1058 (2022) - [c43]Keisuke Fujii, Koh Takeuchi, Atsushi Kuribayashi, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda:
Estimating counterfactual treatment outcomes over time in multi-vehicle simulation. SIGSPATIAL/GIS 2022: 7:1-7:4 - [c42]Matthias Weissenbacher, Samarth Sinha, Animesh Garg, Yoshinobu Kawahara:
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics. ICML 2022: 23645-23667 - [i33]Keisuke Fujii, Koh Takeuchi, Atsushi Kuribayashi, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda:
Estimating counterfactual treatment outcomes over time in complex multi-agent scenarios. CoRR abs/2206.01900 (2022) - [i32]Takuya Konishi, Yoshinobu Kawahara:
Stable Invariant Models via Koopman Spectra. CoRR abs/2207.07475 (2022) - [i31]Tomoharu Iwata, Yoshinobu Kawahara:
Data-driven End-to-end Learning of Pole Placement Control for Nonlinear Dynamics via Koopman Invariant Subspaces. CoRR abs/2208.08883 (2022) - [i30]Tomoharu Iwata, Yoshinobu Kawahara:
Modeling Nonlinear Dynamics in Continuous Time with Inductive Biases on Decay Rates and/or Frequencies. CoRR abs/2212.13033 (2022) - 2021
- [j19]Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Takeshi Katsura, Yoshinobu Kawahara:
Reproducing kernel Hilbert C*-module and kernel mean embeddings. J. Mach. Learn. Res. 22: 267:1-267:56 (2021) - [c41]Naoya Takeishi, Yoshinobu Kawahara:
Learning Dynamics Models with Stable Invariant Sets. AAAI 2021: 9782-9790 - [c40]Tomoharu Iwata, Yoshinobu Kawahara:
Controlling Nonlinear Dynamical Systems with Linear Quadratic Regulator-based Policy Networks in Koopman space. CDC 2021: 5086-5091 - [c39]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:
Learning interaction rules from multi-animal trajectories via augmented behavioral models. NeurIPS 2021: 11108-11122 - [i29]Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Takeshi Katsura, Yoshinobu Kawahara:
Reproducing kernel Hilbert C*-module and kernel mean embeddings. CoRR abs/2101.11410 (2021) - [i28]Tomoharu Iwata, Yoshinobu Kawahara:
Meta-Learning for Koopman Spectral Analysis with Short Time-series. CoRR abs/2102.04683 (2021) - [i27]Naoya Takeishi, Keisuke Fujii, Koh Takeuchi, Yoshinobu Kawahara:
Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections. CoRR abs/2102.09973 (2021) - [i26]Matthias Weissenbacher, Yoshinobu Kawahara:
A Quadratic Actor Network for Model-Free Reinforcement Learning. CoRR abs/2103.06617 (2021) - [i25]Motoya Ohnishi, Isao Ishikawa, Kendall Lowrey, Masahiro Ikeda, Sham M. Kakade, Yoshinobu Kawahara:
Koopman Spectrum Nonlinear Regulator and Provably Efficient Online Learning. CoRR abs/2106.15775 (2021) - [i24]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:
Learning interaction rules from multi-animal trajectories via augmented behavioral models. CoRR abs/2107.05326 (2021) - [i23]Matthias Weissenbacher, Samarth Sinha, Animesh Garg, Yoshinobu Kawahara:
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics. CoRR abs/2111.01365 (2021) - 2020
- [j18]Israr Ul Haq, Keisuke Fujii, Yoshinobu Kawahara:
Dynamic mode decomposition via dictionary learning for foreground modeling in videos. Comput. Vis. Image Underst. 199: 103022 (2020) - [j17]Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Yoichi Matsuo, Yoshinobu Kawahara:
Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise. J. Mach. Learn. Res. 21: 172:1-172:29 (2020) - [j16]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:
Prediction of Compound Bioactivities Using Heat-Diffusion Equation. Patterns 1(9): 100140 (2020) - [c38]Naoya Takeishi, Yoshinobu Kawahara:
Learning Multiple Nonlinear Dynamical Systems with Side Information. CDC 2020: 3206-3211 - [c37]Naoya Takeishi, Yoshinobu Kawahara:
Knowledge-Based Regularization in Generative Modeling. IJCAI 2020: 2390-2396 - [c36]Israr Ul Haq, Keisuke Fujii, Yoshinobu Kawahara:
Dynamic Mode Decomposition via Dictionary Learning for Foreground Modeling in Videos. VISIGRAPP (5: VISAPP) 2020: 476-483 - [i22]Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Takeshi Katsura, Yoshinobu Kawahara:
Analysis via Orthonormal Systems in Reproducing Kernel Hilbert C*-Modules and Applications. CoRR abs/2003.00738 (2020) - [i21]Naoya Takeishi, Yoshinobu Kawahara:
On Anomaly Interpretation via Shapley Values. CoRR abs/2004.04464 (2020) - [i20]Naoya Takeishi, Yoshinobu Kawahara:
Learning Dynamics Models with Stable Invariant Sets. CoRR abs/2006.08935 (2020) - [i19]Keisuke Fujii, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda:
Policy learning with partial observation and mechanical constraints for multi-person modeling. CoRR abs/2007.03155 (2020) - [i18]Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Yoshinobu Kawahara:
Kernel Mean Embeddings of Von Neumann-Algebra-Valued Measures. CoRR abs/2007.14698 (2020) - [i17]Tomoharu Iwata, Yoshinobu Kawahara:
Neural Dynamic Mode Decomposition for End-to-End Modeling of Nonlinear Dynamics. CoRR abs/2012.06191 (2020)
2010 – 2019
- 2019
- [j15]Keisuke Fujii, Yoshinobu Kawahara:
Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables. Neural Networks 117: 94-103 (2019) - [j14]Keisuke Fujii, Yoshinobu Kawahara:
Supervised dynamic mode decomposition via multitask learning. Pattern Recognit. Lett. 122: 7-13 (2019) - [c35]Shogo Hayashi, Yoshinobu Kawahara, Hisashi Kashima:
Active Change-Point Detection. ACML 2019: 1017-1032 - [c34]Takehito Bito, Masashi Hiraoka, Yoshinobu Kawahara:
Learning with Coherence Patterns in Multivariate Time-series Data via Dynamic Mode Decomposition. IJCNN 2019: 1-8 - [c33]Naoya Uematsu, Shunji Umetani, Yoshinobu Kawahara:
An Efficient Branch-and-Cut Algorithm for Approximately Submodular Function Maximization. SMC 2019: 3160-3167 - [c32]Koh Takeuchi, Yuichi Yoshida, Yoshinobu Kawahara:
Variational Inference of Penalized Regression with Submodular Functions. UAI 2019: 1202-1211 - [i16]Naoya Takeishi, Yoshinobu Kawahara:
Regularizing Generative Models Using Knowledge of Feature Dependence. CoRR abs/1902.02068 (2019) - [i15]Naoya Uematsu, Shunji Umetani, Yoshinobu Kawahara:
An efficient branch-and-cut algorithm for approximately submodular function maximization. CoRR abs/1904.12682 (2019) - [i14]Keisuke Fujii, Naoya Takeishi, Motokazu Hojo, Yuki Inaba, Yoshinobu Kawahara:
Physically-interpretable classification of network dynamics for complex collective motions. CoRR abs/1905.04859 (2019) - [i13]Isao Ishikawa, Akinori Tanaka, Masahiro Ikeda, Yoshinobu Kawahara:
Metric on random dynamical systems with vector-valued reproducing kernel Hilbert spaces. CoRR abs/1906.06957 (2019) - [i12]Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Yoichi Matsuo, Yoshinobu Kawahara:
Krylov Subspace Method for Nonlinear Dynamical Systems with Random Noise. CoRR abs/1909.03634 (2019) - 2018
- [j13]Keisuke Fujii, Takeshi Kawasaki, Yuki Inaba, Yoshinobu Kawahara:
Prediction and classification in equation-free collective motion dynamics. PLoS Comput. Biol. 14(11) (2018) - [c31]Naoya Takeishi, Takehisa Yairi, Yoshinobu Kawahara:
Factorially Switching Dynamic Mode Decomposition for Koopman Analysis of Time-Variant Systems. CDC 2018: 6402-6408 - [c30]Isao Ishikawa, Keisuke Fujii, Masahiro Ikeda, Yuka Hashimoto, Yoshinobu Kawahara:
Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators. NeurIPS 2018: 2861-2871 - [i11]Isao Ishikawa, Keisuke Fujii, Masahiro Ikeda, Yuka Hashimoto, Yoshinobu Kawahara:
Metric on Nonlinear Dynamical Systems with Koopman Operators. CoRR abs/1805.12324 (2018) - [i10]Keisuke Fujii, Yoshinobu Kawahara:
Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables. CoRR abs/1808.10551 (2018) - [i9]Naoya Uematsu, Shunji Umetani, Yoshinobu Kawahara:
An efficient branch-and-bound algorithm for submodular function maximization. CoRR abs/1811.04177 (2018) - 2017
- [j12]Hongxing Wang, Yoshinobu Kawahara, Chaoqun Weng, Junsong Yuan:
Representative Selection with Structured Sparsity. Pattern Recognit. 63: 268-278 (2017) - [c29]Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi:
Sparse nonnegative dynamic mode decomposition. ICIP 2017: 2682-2686 - [c28]Naoya Takeishi, Yoshinobu Kawahara, Yasuo Tabei, Takehisa Yairi:
Bayesian Dynamic Mode Decomposition. IJCAI 2017: 2814-2821 - [c27]Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi:
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition. NIPS 2017: 1130-1140 - [c26]Keisuke Fujii, Yuki Inaba, Yoshinobu Kawahara:
Koopman Spectral Kernels for Comparing Complex Dynamics: Application to Multiagent Sport Plays. ECML/PKDD (3) 2017: 127-139 - [c25]Koh Takeuchi, Yoshinobu Kawahara, Tomoharu Iwata:
Structurally Regularized Non-negative Tensor Factorization for Spatio-Temporal Pattern Discoveries. ECML/PKDD (1) 2017: 582-598 - [i8]Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi:
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition. CoRR abs/1710.04340 (2017) - 2016
- [j11]Marina Demeshko, Takashi Washio, Yoshinobu Kawahara, Yuriy Pepyolyshev:
A Novel Continuous and Structural VAR Modeling Approach and Its Application to Reactor Noise Analysis. ACM Trans. Intell. Syst. Technol. 7(2): 24:1-24:22 (2016) - [j10]Bo Xin, Yoshinobu Kawahara, Yizhou Wang, Lingjing Hu, Wen Gao:
Efficient Generalized Fused Lasso and Its Applications. ACM Trans. Intell. Syst. Technol. 7(4): 60:1-60:22 (2016) - [c24]Yoshinobu Kawahara:
Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis. NIPS 2016: 911-919 - 2015
- [j9]Yunzhu Zheng, Haruka Suematsu, Takayuki Itoh, Ryohei Fujimaki, Satoshi Morinaga, Yoshinobu Kawahara:
Scatterplot layout for high-dimensional data visualization. J. Vis. 18(1): 111-119 (2015) - [c23]Yoshinobu Kawahara, Rishabh K. Iyer, Jeff A. Bilmes:
On Approximate Non-submodular Minimization via Tree-Structured Supermodularity. AISTATS 2015 - [c22]Shinichi Yamagiwa, Yoshinobu Kawahara, Noriyuki Tabuchi, Yoshinobu Watanabe, Takeshi Naruo:
Skill grouping method: Mining and clustering skill differences from body movement BigData. IEEE BigData 2015: 2525-2534 - [c21]Koh Takeuchi, Yoshinobu Kawahara, Tomoharu Iwata:
Higher Order Fused Regularization for Supervised Learning with Grouped Parameters. ECML/PKDD (1) 2015: 577-593 - [i7]Yoshinobu Kawahara, Yutaro Yamaguchi:
Parametric Maxflows for Structured Sparse Learning with Convex Relaxations of Submodular Functions. CoRR abs/1509.03946 (2015) - 2014
- [c20]Bo Xin, Yoshinobu Kawahara, Yizhou Wang, Wen Gao:
Efficient Generalized Fused Lasso and its Application to the Diagnosis of Alzheimer's Disease. AAAI 2014: 2163-2169 - [c19]Yoshinobu Kawahara, Tetsuji Kuboyama, Hiroshi Sakamoto:
Workshop on Graph-Based Algorithms for Big Data and Its Applications (GABA2014). JSAI-isAI Workshops 2014: 293-295 - [c18]Mahito Sugiyama, Chloé-Agathe Azencott, Dominik G. Grimm, Yoshinobu Kawahara, Karsten M. Borgwardt:
Multi-Task Feature Selection on Multiple Networks via Maximum Flows. SDM 2014: 199-207 - [i6]Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara:
Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM. CoRR abs/1401.5636 (2014) - [i5]Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara:
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model. CoRR abs/1408.2038 (2014) - 2013
- [j8]Chloé-Agathe Azencott, Dominik G. Grimm, Mahito Sugiyama, Yoshinobu Kawahara, Karsten M. Borgwardt:
Efficient network-guided multi-locus association mapping with graph cuts. Bioinform. 29(13): 171-179 (2013) - [j7]Akiko Takeda, Mahesan Niranjan, Jun-ya Gotoh, Yoshinobu Kawahara:
Simultaneous pursuit of out-of-sample performance and sparsity in index tracking portfolios. Comput. Manag. Sci. 10(1): 21-49 (2013) - [j6]Yasuhiro Sogawa, Tsuyoshi Ueno, Yoshinobu Kawahara, Takashi Washio:
Active learning for noisy oracle via density power divergence. Neural Networks 46: 133-143 (2013) - [c17]Marina Demeshko, Takashi Washio, Yoshinobu Kawahara:
A Novel Structural AR Modeling Approach for a Continuous Time Linear Markov System. ICDM Workshops 2013: 104-113 - [c16]Haruka Suematsu, Yunzhu Zheng, Takayuki Itoh, Ryohei Fujimaki, Satoshi Morinaga, Yoshinobu Kawahara:
Arrangement of Low-Dimensional Parallel Coordinate Plots for High-Dimensional Data Visualization. IV 2013: 59-65 - [c15]Kiyohito Nagano, Yoshinobu Kawahara:
Structured Convex Optimization under Submodular Constraints. UAI 2013 - [i4]Kiyohito Nagano, Yoshinobu Kawahara:
Structured Convex Optimization under Submodular Constraints. CoRR abs/1309.6850 (2013) - 2012
- [j5]Satoshi Hara, Yoshinobu Kawahara, Takashi Washio, Paul von Bünau, Terumasa Tokunaga, Kiyohumi Yumoto:
Separation of stationary and non-stationary sources with a generalized eigenvalue problem. Neural Networks 33: 7-20 (2012) - [j4]Yoshinobu Kawahara, Masashi Sugiyama:
Sequential change-point detection based on direct density-ratio estimation. Stat. Anal. Data Min. 5(2): 114-127 (2012) - [c14]Yasuhiro Sogawa, Tsuyoshi Ueno, Yoshinobu Kawahara, Takashi Washio:
Robust Active Learning for Linear Regression via Density Power Divergence. ICONIP (3) 2012: 594-602 - [c13]Tsuyoshi Ueno, Kohei Hayashi, Takashi Washio, Yoshinobu Kawahara:
Weighted Likelihood Policy Search with Model Selection. NIPS 2012: 2366-2374 - [i3]Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara:
Discovering causal structures in binary exclusive-or skew acyclic models. CoRR abs/1202.3736 (2012) - 2011
- [j3]Yoshinobu Kawahara, Shohei Shimizu, Takashi Washio:
Analyzing relationships among ARMA processes based on non-Gaussianity of external influences. Neurocomputing 74(12-13): 2212-2221 (2011) - [j2]Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvärinen, Yoshinobu Kawahara, Takashi Washio, Patrik O. Hoyer, Kenneth Bollen:
DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model. J. Mach. Learn. Res. 12: 1225-1248 (2011) - [j1]Yoshinobu Kawahara, Kiyohito Nagano, Yoshio Okamoto:
Submodular fractional programming for balanced clustering. Pattern Recognit. Lett. 32(2): 235-243 (2011) - [c12]Kiyohito Nagano, Yoshinobu Kawahara, Kazuyuki Aihara:
Size-constrained Submodular Minimization through Minimum Norm Base. ICML 2011: 977-984 - [c11]Yoshinobu Kawahara, Takashi Washio:
Prismatic Algorithm for Discrete D.C. Programming Problem. NIPS 2011: 2106-2114 - [c10]Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara:
Discovering causal structures in binary exclusive-or skew acyclic models. UAI 2011: 373-382 - [i2]Yoshinobu Kawahara, Takashi Washio:
Prismatic Algorithm for Discrete D.C. Programming Problems. CoRR abs/1108.4217 (2011) - 2010
- [c9]Satoshi Hara, Yoshinobu Kawahara, Takashi Washio, Paul von Bünau:
Stationary Subspace Analysis as a Generalized Eigenvalue Problem. ICONIP (1) 2010: 422-429 - [c8]Masao Joko, Yoshinobu Kawahara, Takehisa Yairi:
Learning Non-linear Dynamical Systems by Alignment of Local Linear Models. ICPR 2010: 1084-1087 - [c7]Yasuhiro Sogawa, Shohei Shimizu, Yoshinobu Kawahara, Takashi Washio:
An experimental comparison of linear non-Gaussian causal discovery methods and their variants. IJCNN 2010: 1-8 - [c6]Kiyohito Nagano, Yoshinobu Kawahara, Satoru Iwata:
Minimum Average Cost Clustering. NIPS 2010: 1759-1767 - [i1]Yoshinobu Kawahara, Kenneth Bollen, Shohei Shimizu, Takashi Washio:
GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables. CoRR abs/1006.5041 (2010)
2000 – 2009
- 2009
- [c5]Yoshinobu Kawahara, Kiyohito Nagano, Koji Tsuda, Jeff A. Bilmes:
Submodularity Cuts and Applications. NIPS 2009: 916-924 - [c4]Yoshinobu Kawahara, Masashi Sugiyama:
Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation. SDM 2009: 389-400 - [c3]Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara:
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model. UAI 2009: 506-513 - 2007
- [c2]Yoshinobu Kawahara, Takehisa Yairi, Kazuo Machida:
Change-Point Detection in Time-Series Data Based on Subspace Identification. ICDM 2007: 559-564 - 2006
- [c1]Yoshinobu Kawahara, Takehisa Yairi, Kazuo Machida:
A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems. NIPS 2006: 665-672
Coauthor Index