Yoshinobu Kawahara

According to our database, Yoshinobu Kawahara authored at least 99 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

MANet: Mixed Attention Network for Visual Explanation.

Jingjing Bai, Yoshinobu Kawahara

New Gener. Comput., November, 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

Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling.

Keisuke Fujii, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda

Neural Networks, 2024

Change-Point Detection in Industrial Data Streams based on Online Dynamic Mode Decomposition with Control.

Marek Wadinger, Michal Kvasnica, 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

Koopman operators with intrinsic observables in rigged reproducing kernel Hilbert spaces.

Isao Ishikawa, Yuka Hashimoto, Masahiro Ikeda, Yoshinobu Kawahara

CoRR, 2024

Glocal Hypergradient Estimation with Koopman Operator.

Ryuichiro Hataya, Yoshinobu Kawahara

CoRR, 2024

SiT: Symmetry-invariant Transformers for Generalisation in Reinforcement Learning.

Matthias Weissenbacher, Rishabh Agarwal, Yoshinobu Kawahara

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Adaptive Action Supervision in Reinforcement Learning from Real-World Multi-Agent Demonstrations.

Keisuke Fujii, Kazushi Tsutsui, Atom Scott, Hiroshi Nakahara, Naoya Takeishi, Yoshinobu Kawahara

Proceedings of the 16th International Conference on Agents and Artificial Intelligence, 2024

2023

euMMD: efficiently computing the MMD two-sample test statistic for univariate data.

Dean A. Bodenham, Yoshinobu Kawahara

Stat. Comput., October, 2023

Stable invariant models via Koopman spectra.

Takuya Konishi, Yoshinobu Kawahara

Neural Networks, August, 2023

A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores.

Naoya Takeishi, Yoshinobu Kawahara

Trans. Mach. Learn. Res., 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

Many-body Approximation for Non-negative Tensors.

Kazu Ghalamkari, Mahito Sugiyama, Yoshinobu Kawahara

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Discriminant Dynamic Mode Decomposition for Labeled Spatiotemporal Data Collections.

Naoya Takeishi, Keisuke Fujii, Koh Takeuchi, Yoshinobu Kawahara

SIAM J. Appl. Dyn. Syst., 2022

Predicting behavior through dynamic modes in resting-state fMRI data.

Shigeyuki Ikeda, Koki Kawano, Soichi Watanabe, Okito Yamashita, Yoshinobu Kawahara

NeuroImage, 2022

Dynamic mode decomposition via convolutional autoencoders for dynamics modeling in videos.

Israr Ul Haq, Tomoharu Iwata, Yoshinobu Kawahara

Comput. Vis. Image Underst., 2022

Modeling Nonlinear Dynamics in Continuous Time with Inductive Biases on Decay Rates and/or Frequencies.

Tomoharu Iwata, Yoshinobu Kawahara

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

Estimating counterfactual treatment outcomes over time in complex multi-agent scenarios.

Keisuke Fujii, Koh Takeuchi, Atsushi Kuribayashi, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda

CoRR, 2022

Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics.

Matthias Weissenbacher, Samarth Sinha, Animesh Garg, Yoshinobu Kawahara

Proceedings of the International Conference on Machine Learning, 2022

Estimating counterfactual treatment outcomes over time in multi-vehicle simulation.

Keisuke Fujii, Koh Takeuchi, Atsushi Kuribayashi, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda

Proceedings of the 30th International Conference on Advances in Geographic Information Systems, 2022

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

Koopman Spectrum Nonlinear Regulator and Provably Efficient Online Learning.

Motoya Ohnishi, Isao Ishikawa, Kendall Lowrey, Masahiro Ikeda, Sham M. Kakade, Yoshinobu Kawahara

CoRR, 2021

A Quadratic Actor Network for Model-Free Reinforcement Learning.

Matthias Weissenbacher, Yoshinobu Kawahara

CoRR, 2021

Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections.

Naoya Takeishi, Keisuke Fujii, Koh Takeuchi, Yoshinobu Kawahara

CoRR, 2021

Meta-Learning for Koopman Spectral Analysis with Short Time-series.

Tomoharu Iwata, Yoshinobu Kawahara

CoRR, 2021

Learning interaction rules from multi-animal trajectories via augmented behavioral models.

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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Controlling Nonlinear Dynamical Systems with Linear Quadratic Regulator-based Policy Networks in Koopman space.

Tomoharu Iwata, Yoshinobu Kawahara

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

Learning Dynamics Models with Stable Invariant Sets.

Naoya Takeishi, Yoshinobu Kawahara

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

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

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

Dynamic mode decomposition via dictionary learning for foreground modeling in videos.

Israr Ul Haq, Keisuke Fujii, Yoshinobu Kawahara

Comput. Vis. Image Underst., 2020

Neural Dynamic Mode Decomposition for End-to-End Modeling of Nonlinear Dynamics.

Tomoharu Iwata, Yoshinobu Kawahara

CoRR, 2020

Kernel Mean Embeddings of Von Neumann-Algebra-Valued Measures.

Yuka Hashimoto, Isao Ishikawa, Masahiro Ikeda, Fuyuta Komura, Yoshinobu Kawahara

CoRR, 2020

Policy learning with partial observation and mechanical constraints for multi-person modeling.

Keisuke Fujii, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda

CoRR, 2020

On Anomaly Interpretation via Shapley Values.

Naoya Takeishi, Yoshinobu Kawahara

CoRR, 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

Knowledge-Based Regularization in Generative Modeling.

Naoya Takeishi, Yoshinobu Kawahara

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Learning Multiple Nonlinear Dynamical Systems with Side Information.

Naoya Takeishi, Yoshinobu Kawahara

Proceedings of the 59th IEEE Conference on Decision and Control, 2020

2019

Supervised dynamic mode decomposition via multitask learning.

Keisuke Fujii, Yoshinobu Kawahara

Pattern Recognit. Lett., 2019

Dynamic mode decomposition in vector-valued reproducing kernel Hilbert spaces for extracting dynamical structure among observables.

Keisuke Fujii, Yoshinobu Kawahara

Neural Networks, 2019

Metric on random dynamical systems with vector-valued reproducing kernel Hilbert spaces.

Isao Ishikawa, Akinori Tanaka, Masahiro Ikeda, Yoshinobu Kawahara

CoRR, 2019

Physically-interpretable classification of network dynamics for complex collective motions.

Keisuke Fujii, Naoya Takeishi, Motokazu Hojo, Yuki Inaba, Yoshinobu Kawahara

CoRR, 2019

Regularizing Generative Models Using Knowledge of Feature Dependence.

Naoya Takeishi, Yoshinobu Kawahara

CoRR, 2019

Variational Inference of Penalized Regression with Submodular Functions.

Koh Takeuchi, Yuichi Yoshida, Yoshinobu Kawahara

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

An Efficient Branch-and-Cut Algorithm for Approximately Submodular Function Maximization.

Naoya Uematsu, Shunji Umetani, Yoshinobu Kawahara

Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics, 2019

Learning with Coherence Patterns in Multivariate Time-series Data via Dynamic Mode Decomposition.

Takehito Bito, Masashi Hiraoka, Yoshinobu Kawahara

Proceedings of the International Joint Conference on Neural Networks, 2019

Active Change-Point Detection.

Shogo Hayashi, Yoshinobu Kawahara, Hisashi Kashima

Proceedings of The 11th Asian Conference on Machine Learning, 2019

2018

Prediction and classification in equation-free collective motion dynamics.

Keisuke Fujii, Takeshi Kawasaki, Yuki Inaba, Yoshinobu Kawahara

PLoS Comput. Biol., 2018

An efficient branch-and-bound algorithm for submodular function maximization.

Naoya Uematsu, Shunji Umetani, Yoshinobu Kawahara

CoRR, 2018

Metric on Nonlinear Dynamical Systems with Koopman Operators.

Isao Ishikawa, Keisuke Fujii, Masahiro Ikeda, Yuka Hashimoto, Yoshinobu Kawahara

CoRR, 2018

Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators.

Isao Ishikawa, Keisuke Fujii, Masahiro Ikeda, Yuka Hashimoto, Yoshinobu Kawahara

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Factorially Switching Dynamic Mode Decomposition for Koopman Analysis of Time-Variant Systems.

Naoya Takeishi, Takehisa Yairi, Yoshinobu Kawahara

Proceedings of the 57th IEEE Conference on Decision and Control, 2018

2017

Representative Selection with Structured Sparsity.

Hongxing Wang, Yoshinobu Kawahara, Chaoqun Weng, Junsong Yuan

Pattern Recognit., 2017

Structurally Regularized Non-negative Tensor Factorization for Spatio-Temporal Pattern Discoveries.

Koh Takeuchi, Yoshinobu Kawahara, Tomoharu Iwata

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Koopman Spectral Kernels for Comparing Complex Dynamics: Application to Multiagent Sport Plays.

Keisuke Fujii, Yuki Inaba, Yoshinobu Kawahara

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition.

Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Bayesian Dynamic Mode Decomposition.

Naoya Takeishi, Yoshinobu Kawahara, Yasuo Tabei, Takehisa Yairi

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Sparse nonnegative dynamic mode decomposition.

Naoya Takeishi, Yoshinobu Kawahara, Takehisa Yairi

Proceedings of the 2017 IEEE International Conference on Image Processing, 2017

2016

Efficient Generalized Fused Lasso and Its Applications.

Bo Xin, Yoshinobu Kawahara, Yizhou Wang, Lingjing Hu, Wen Gao

ACM Trans. Intell. Syst. Technol., 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

Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis.

Yoshinobu Kawahara

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Scatterplot layout for high-dimensional data visualization.

Yunzhu Zheng, Haruka Suematsu, Takayuki Itoh, Ryohei Fujimaki, Satoshi Morinaga, Yoshinobu Kawahara

J. Vis., 2015

Parametric Maxflows for Structured Sparse Learning with Convex Relaxations of Submodular Functions.

Yoshinobu Kawahara, Yutaro Yamaguchi

CoRR, 2015

Higher Order Fused Regularization for Supervised Learning with Grouped Parameters.

Koh Takeuchi, Yoshinobu Kawahara, Tomoharu Iwata

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Skill grouping method: Mining and clustering skill differences from body movement BigData.

Shinichi Yamagiwa, Yoshinobu Kawahara, Noriyuki Tabuchi, Yoshinobu Watanabe, Takeshi Naruo

Proceedings of the 2015 IEEE International Conference on Big Data (IEEE BigData 2015), Santa Clara, CA, USA, October 29, 2015

On Approximate Non-submodular Minimization via Tree-Structured Supermodularity.

Yoshinobu Kawahara, Rishabh K. Iyer, Jeff A. Bilmes

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

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

Multi-Task Feature Selection on Multiple Networks via Maximum Flows.

Mahito Sugiyama, Chloé-Agathe Azencott, Dominik G. Grimm, Yoshinobu Kawahara, Karsten M. Borgwardt

Proceedings of the 2014 SIAM International Conference on Data Mining, 2014

Workshop on Graph-Based Algorithms for Big Data and Its Applications (GABA2014).

Yoshinobu Kawahara, Tetsuji Kuboyama, Hiroshi Sakamoto

Proceedings of the New Frontiers in Artificial Intelligence, 2014

Efficient Generalized Fused Lasso and its Application to the Diagnosis of Alzheimer's Disease.

Bo Xin, Yoshinobu Kawahara, Yizhou Wang, Wen Gao

Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013

Active learning for noisy oracle via density power divergence.

Yasuhiro Sogawa, Tsuyoshi Ueno, Yoshinobu Kawahara, Takashi Washio

Neural Networks, 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

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

Structured Convex Optimization under Submodular Constraints.

Kiyohito Nagano, Yoshinobu Kawahara

Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 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

Proceedings of the 17th International Conference on Information Visualisation, 2013

A Novel Structural AR Modeling Approach for a Continuous Time Linear Markov System.

Marina Demeshko, Takashi Washio, Yoshinobu Kawahara

Proceedings of the 13th IEEE International Conference on Data Mining Workshops, 2013

2012

Sequential change-point detection based on direct density-ratio estimation.

Yoshinobu Kawahara, Masashi Sugiyama

Stat. Anal. Data Min., 2012

Separation of stationary and non-stationary sources with a generalized eigenvalue problem.

Satoshi Hara, Yoshinobu Kawahara, Takashi Washio, Paul von Bünau, Terumasa Tokunaga, Kiyohumi Yumoto

Neural Networks, 2012

Weighted Likelihood Policy Search with Model Selection.

Tsuyoshi Ueno, Kohei Hayashi, Takashi Washio, Yoshinobu Kawahara

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Robust Active Learning for Linear Regression via Density Power Divergence.

Yasuhiro Sogawa, Tsuyoshi Ueno, Yoshinobu Kawahara, Takashi Washio

Proceedings of the Neural Information Processing - 19th International Conference, 2012

2011

Submodular fractional programming for balanced clustering.

Yoshinobu Kawahara, Kiyohito Nagano, Yoshio Okamoto

Pattern Recognit. Lett., 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

Analyzing relationships among ARMA processes based on non-Gaussianity of external influences.

Yoshinobu Kawahara, Shohei Shimizu, Takashi Washio

Neurocomputing, 2011

Prismatic Algorithm for Discrete D.C. Programming Problems

Yoshinobu Kawahara, Takashi Washio

CoRR, 2011

Discovering causal structures in binary exclusive-or skew acyclic models.

Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara

Proceedings of the UAI 2011, 2011

Prismatic Algorithm for Discrete D.C. Programming Problem.

Yoshinobu Kawahara, Takashi Washio

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Size-constrained Submodular Minimization through Minimum Norm Base.

Kiyohito Nagano, Yoshinobu Kawahara, Kazuyuki Aihara

Proceedings of the 28th International Conference on Machine Learning, 2011

2010

GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables

Yoshinobu Kawahara, Kenneth Bollen, Shohei Shimizu, Takashi Washio

CoRR, 2010

Minimum Average Cost Clustering.

Kiyohito Nagano, Yoshinobu Kawahara, Satoru Iwata

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

An experimental comparison of linear non-Gaussian causal discovery methods and their variants.

Yasuhiro Sogawa, Shohei Shimizu, Yoshinobu Kawahara, Takashi Washio

Proceedings of the International Joint Conference on Neural Networks, 2010

Learning Non-linear Dynamical Systems by Alignment of Local Linear Models.

Masao Joko, Yoshinobu Kawahara, Takehisa Yairi

Proceedings of the 20th International Conference on Pattern Recognition, 2010

Stationary Subspace Analysis as a Generalized Eigenvalue Problem.

Satoshi Hara, Yoshinobu Kawahara, Takashi Washio, Paul von Bünau

Proceedings of the Neural Information Processing. Theory and Algorithms, 2010

2009

A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model.

Shohei Shimizu, Aapo Hyvärinen, Yoshinobu Kawahara

Proceedings of the UAI 2009, 2009

Change-Point Detection in Time-Series Data by Direct Density-Ratio Estimation.

Yoshinobu Kawahara, Masashi Sugiyama

Proceedings of the SIAM International Conference on Data Mining, 2009

Submodularity Cuts and Applications.

Yoshinobu Kawahara, Kiyohito Nagano, Koji Tsuda, Jeff A. Bilmes

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2007

Change-Point Detection in Time-Series Data Based on Subspace Identification.

Yoshinobu Kawahara, Takehisa Yairi, Kazuo Machida

Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), 2007

2006

A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems.

Yoshinobu Kawahara, Takehisa Yairi, Kazuo Machida

Proceedings of the Advances in Neural Information Processing Systems 19, 2006