Updated on 2024/07/26

 
ISHIBASHI Hideaki
 
Scopus Paper Info  
Total Paper Count: 0  Total Citation Count: 0  h-index: 4

Citation count denotes the number of citations in papers published for a particular year.

Affiliation
Graduate School of Life Science and Systems Engineering Department of Human Intelligence Systems
Job
Assistant Professor
External link

Degree

  • Kyushu Institute of Technology  -  Doctor of Information Engineering   2018.03

Biography in Kyutech

  • 2019.04
     

    Kyushu Institute of Technology   Graduate School of Life Science and Systems Engineering   Department of Human Intelligence Systems   Assistant Professor  

Papers

  • Principal Component Analysis for Gaussian Process Posteriors Reviewed International journal

    Ishibashi H., Akaho S.

    Neural Computation   34 ( 5 )   1189 - 1219   2022.04

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)

    DOI： 10.1162/neco_a_01489

    Scopus

    CiNii Research

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  • Stopping criterion for active learning based on deterministic generalization bounds Reviewed International journal

    Ishibashi H., Hino H.

    Proceedings of Machine Learning Research   108   386 - 397   2020.01

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

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  • A stopping criterion for level set estimation Reviewed

    ISHIBASHI Hideaki, MATSUI Kota, KUTSUKAKE Kentaro, HINO Hideitsu

    Proceedings of the Annual Conference of JSAI ( The Japanese Society for Artificial Intelligence )   JSAI2024 ( 0 )   2M5OS2401 - 2M5OS2401   2024.01

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    Authorship：Lead author,　Corresponding author   Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)

    <p>Level set estimation is one of the adaptive experimental design that determines the next measurement point by using the obtained measurement results so far, and its task is to estimate the regions that do not satisfy the desired level using as few data as possible. Level set estimation considers a black box function with each measurement point as an input and the corresponding measurement result as an output, and predicts whether unmeasurement point exceeds the threshold using a surrogate function estimated from the dataset. The efficiency of level set estimation depends on (1) the acquisition function that determines the next measurement point and (2) the timing at which level set estimation is stopped. This study proposes a stopping criterion for level set estimation based on the probability that the surrogate function exceeds the threshold value. The proposed stopping criterion can guarantee a tail probability that the surrogate function exceeds the threshold for any acquisition function. This paper shows that the proposed stopping criterion can efficiently stop level set estimation for several test functions.</p>

    DOI： 10.11517/pjsai.jsai2024.0_2m5os2401

    CiNii Research

  • Meta-modeling of latent variable models Reviewed

    FURUKAWA Tetsuo, ISHIBASHI Hideaki

    Proceedings of the Annual Conference of JSAI ( The Japanese Society for Artificial Intelligence )   JSAI2024 ( 0 )   2M5OS2402 - 2M5OS2402   2024.01

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    Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)

    <p>In this presentation, we discuss the learning theory behind the meta-modeling of latent variable models. Meta-modeling, as addressed in this presentation, represents a form of meta-learning. It involves the challenge of estimating a meta-model that describes a set of models derived from multiple learning tasks. A key challenge in the meta-modeling of latent variable models is ensuring consistency in the latent variables across different tasks. This presentation proposes a meta-learning method for latent variable models and explores its theoretical implications from the perspective of optimal transport distance.</p>

    DOI： 10.11517/pjsai.jsai2024.0_2m5os2402

    CiNii Research

  • ATNAS: Automatic Termination for Neural Architecture Search Reviewed International journal

    Sakamoto K., Ishibashi H., Sato R., Shirakawa S., Akimoto Y., Hino H.

    Neural Networks   166   446 - 458   2023.09

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    Language：English   Publishing type：Research paper (scientific journal)

    DOI： 10.1016/j.neunet.2023.07.011

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  • A stopping criterion for Bayesian optimization by the gap of expected minimum simple regrets Reviewed International journal

    Ishibashi H., Karasuyama M., Takeuchi I., Hino H.

    Proceedings of Machine Learning Research   206   6463 - 6497   2023.01

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

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  • End-condition for solution small angle X-ray scattering measurements by kernel density estimation Reviewed International journal

    Hiroshi Sekiguchi, Noboru Ohta, Hideaki Ishibashi, Hideitsu Hino, Masaichiro Mizumaki

    Science and Technology of Advanced Materials: Methods   2022.12

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    Language：English   Publishing type：Research paper (scientific journal)

    DOI： 10.1080/27660400.2022.2140021

    DOI： 10.1080/27660400.2022.2140021

  • Multi-task manifold learning for small sample size datasets Reviewed

    Ishibashi H., Higa K., Furukawa T.

    Neurocomputing   473   138 - 157   2022.02

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    Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)

    DOI： 10.1016/j.neucom.2021.11.043

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  • Low-rank kernel decomposition for scalable manifold modeling Reviewed International journal

    Miyazaki K., Takano S., Tsuno R., Ishibashi H., Furukawa T.

    2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2022   2022.01

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    Language：English   Publishing type：Research paper (international conference proceedings)

    DOI： 10.1109/SCISISIS55246.2022.10001865

    Scopus

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  • Stopping criterion for Neural Architecture Search Reviewed

    SAKAMOTO Kotaro Sakamoto, ISHIBASHI Hideaki, SATO Rei, SHIRAKAWA Shinichi, AKIMOTO Yohei, HINO Hideitsu

    Proceedings of the Annual Conference of JSAI ( The Japanese Society for Artificial Intelligence )   JSAI2022 ( 0 )   3J4OS3b01 - 3J4OS3b01   2022.01

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    Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)

    <p>Neural architecture search (NAS) is a framework for automating the design process of a neural network structure. While the recent one-shot approaches have reduced the search cost, there still exists an inherent trade-off between cost and performance. It is important to appropriately stop the search and further minimise the high cost of NAS. On the other hand, heuristic early-stopping strategies have been proposed to overcome the well-known performance degradation of the one-shot approach, particularly differentiable architecture search (DARTS). In this paper, we propose a more versatile and principled early-stopping criterion on the basis of the evaluation of a gap between expectation values of generalisation errors of the previous and current search steps with respect to the architecture parameters. The stopping threshold is automatically determined at each search epoch without cost. In numerical experiments, we demonstrate the effectiveness of the proposed method. We stop the one-shot NAS algorithms such as ASNG-NAS and DARTS and evaluate the acquired architectures on the benchmark datasets: NAS-Bench-201 and NATS-Bench. Our algorithm has been shown to reduce the cost of the search process while maintaining a high performance.</p>

    DOI： 10.11517/pjsai.jsai2022.0_3j4os3b01

    CiNii Research

  • Automated stopping of spectral measurements with active learning Reviewed

    UENO Tetsuro, ISHIBASHI Hideaki, HINO Hideitsu, ONO Kanta

    Proceedings of the Annual Conference of JSAI ( The Japanese Society for Artificial Intelligence )   JSAI2022 ( 0 )   3Yin208 - 3Yin208   2022.01

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    Language：Japanese   Publishing type：Research paper (conference, symposium, etc.)

    <p>There is a need for improvement of the efficiency, automation, and autonomy of various experiments in materials science with the recent rise of materials informatics. We have developed a method to improve the efficiency of spectral measurements, one of general experimental techniques in materials science, by using active learning. By using active learning to sequentially measure the energy points with the maximum of the acquisition function, we have achieved automated spectral measurement under optimal conditions without the intervention of an experimenter. By employing a stopping criterion based on the upper bound of the expected generalization error of the Gaussian process regression, the measurement can be automatically stopped regardless of the type of spectrum. This method allows us to obtain the materials information of equivalent quality with fewer measurement points compared to the conventional spectral measurement.</p>

    DOI： 10.11517/pjsai.jsai2022.0_3yin208

    CiNii Research

  • Automated stopping criterion for spectral measurements with active learning Reviewed

    Ueno T., Ishibashi H., Hino H., Ono K.

    npj Computational Materials   7 ( 1 )   2021.12

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    Language：English   Publishing type：Research paper (scientific journal)

    The automated stopping of a spectral measurement with active learning is proposed. The optimal stopping of the measurement is realised with a stopping criterion based on the upper bound of the posterior average of the generalisation error of the Gaussian process regression. It is revealed that the automated stopping criterion of the spectral measurement gives an approximated X-ray absorption spectrum with sufficient accuracy and reduced data size. The proposed method is not only a proof-of-concept of the optimal stopping problem in active learning but also the key to enhancing the efficiency of spectral measurements for high-throughput experiments in the era of materials informatics.

    DOI： 10.1038/s41524-021-00606-5

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  • Visual analytics of set data for knowledge discovery and member selection support Reviewed

    Watanabe R., Ishibashi H., Furukawa T.

    Decision Support Systems   152   2021.01

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    Language：English   Publishing type：Research paper (scientific journal)

    DOI： 10.1016/j.dss.2021.113635

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  • Multi-task learning for Self-Organizing Maps Reviewed

    Kazushi Higa, Hideaki Ishibashi, Tetsuo Furukawa

    Proceedings of Joint 10th International Conference on Soft Computing and Intelligent Systems and19th International Symposium on Advanced Intelligent Systemsin conjunction with Intelligent Systems Workshop 2018   2018.12

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    Language：English   Publishing type：Research paper (international conference proceedings)

  • Non-parametriccontinuous Self-Organizing Map Reviewed

    Ryuji Watanabe, Hideaki Ishibashi, Tohru Iwasaki, Tetsuo Furukawa

    Proceedings of Joint 10th International Conference on Soft Computing and Intelligent Systems and19th International Symposium on Advanced Intelligent Systemsin conjunction with Intelligent Systems Workshop 2018   2018.12

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    Language：English   Publishing type：Research paper (international conference proceedings)

  • Multi-task manifold learning using hierarchical modeling for insufficient samples Reviewed International journal

    Hideaki Ishibashi, Kazushi Higa, Tetsuo Furukawa

    Lecture Notes in Computer Science   11303   388 - 398   2018.11

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    Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)

    DOI： 10.1007/978-3-030-04182-3_34

    Kyutacar

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  • Visualization method of viewpoints latent in a dataset Reviewed International journal

    Hideaki Ishibashi

    Lecture Notes in Computer Science   11303   638 - 647   2018.11

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    Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

    DOI： 10.1007/978-3-030-04182-3_56

    Kyutacar

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  • Hierarchical tensor SOM network for multilevel multigroup analysis Reviewed

    Hideaki Ishibashi, Tetsuo Furukawa)

    Proceedings of Neural Processing Letters   47 ( 3 )   1011 - 1025   2018.06

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    Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)

    DOI： 10.1007/s11063-017-9643-1

    Kyutacar

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  • Hierarchical Tensor Manifold Modeling for Multi-Group Analysis Reviewed

    ISHIBASHI Hideaki, ERA Masayoshi, FURUKAWA Tetsuo

    101 ( 11 )   1745 - 1755   2018.01

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    Language：English   Publishing type：Research paper (scientific journal)

    DOI： 10.1587/transfun.E101.A.1745

    CiNii Article

    Other Link： https://ci.nii.ac.jp/naid/130007502924

  • Multilevel-Multigroup Analysis Discovering Member Correspondence between Groups Reviewed

    Hideaki Ishibashi, Masayoshi Era, Ryota Shinriki, Hirohisa Isogai, Tetsuo Furukawa

    Proceedings of 2017 International Workshop on Smart Info-Media Systems in Asia   2017.09

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    Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)

    Kyutacar

  • Self-Organizing Maps for Multi-system and Multi-view Datasets Reviewed International journal

    Ishibashi H., Furukawa T.

    Proceedings - 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems and 2016 17th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2016   343 - 348   2016.12

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    Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)

    DOI： 10.1109/SCIS-ISIS.2016.0078

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  • Multilevel-Multigroup analysis using a hierarchical tensor SOM network Reviewed International journal

    Hideaki Ishibashi, Ryota Shinriki, Hirohisa Isogai, Tetsuo Furukawa

    Lecture Notes in Computer Science   9949   459 - 466   2016.10

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    Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)

    DOI： 10.1007/978-3-319-46675-0_50

    Kyutacar

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  • Rating-scale questionnaire survey analysis using SOM-based nonlinear tensor decomposition Reviewed

    Hideaki Ishibashi, Tohru Iwasaki, Yohsuke Date, Tetsuo Furukawa

    Proceedings of Joint 8th International Conference on Soft Computing and Intelligent Systems and 17th International Symposium on Advanced Intelligent Systems2016   912 - 915   2016.08

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    Authorship：Lead author   Language：English   Publishing type：Research paper (international conference proceedings)

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Lectures

  • 能動学習の停止基準

    第42回IBISML研究会オーガナイズドセッション  2021.03 

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    Language：Japanese   Presentation type：Invited lecture  

Grants-in-Aid for Scientific Research

  • 情報幾何学的メタモデリングに基づいた変分推論法のマルチタスク学習

    Grant number：24K15088  2024.04 - 2027.03   基盤研究(C)

  • Energy based model集合のメタモデリング

    Grant number：22K17951  2022.04 - 2023.03   若手研究

  • 情報幾何的階層モデリング

    Grant number：20K19865  2020.04 - 2022.03   若手研究