| Stopping criterion for active learning based on deterministic generalization bounds H Ishibashi, H Hino International Conference on Artificial Intelligence and Statistics, 386-397, 2020 | 32 | 2020 |
| Automated stopping criterion for spectral measurements with active learning T Ueno, H Ishibashi, H Hino, K Ono npj Computational Materials 7 (1), 139, 2021 | 22 | 2021 |
| Multi-task manifold learning for small sample size datasets H Ishibashi, K Higa, T Furukawa Neurocomputing 473, 138-157, 2022 | 15 | 2022 |
| Stopping criterion for active learning based on error stability H Ishibashi, H Hino arXiv preprint arXiv:2104.01836, 2021 | 13 | 2021 |
| A stopping criterion for Bayesian optimization by the gap of expected minimum simple regrets H Ishibashi, M Karasuyama, I Takeuchi, H Hino International Conference on Artificial Intelligence and Statistics, 6463-6497, 2023 | 8 | 2023 |
| Visual analytics of set data for knowledge discovery and member selection support R Watanabe, H Ishibashi, T Furukawa Decision Support Systems 152, 113635, 2022 | 7 | 2022 |
| Hierarchical tensor SOM network for multilevel–multigroup analysis H Ishibashi, T Furukawa Neural Processing Letters 47, 1011-1025, 2018 | 7 | 2018 |
| Principal component analysis for Gaussian process posteriors H Ishibashi, S Akaho Neural Computation 34 (5), 1189-1219, 2022 | 5 | 2022 |
| Multilevel–multigroup analysis using a hierarchical tensor SOM network H Ishibashi, R Shinriki, H Isogai, T Furukawa Neural Information Processing: 23rd International Conference, ICONIP 2016 …, 2016 | 5 | 2016 |
| ATNAS: Automatic Termination for Neural Architecture Search K Sakamoto, H Ishibashi, R Sato, S Shirakawa, Y Akimoto, H Hino Neural Networks 166, 446-458, 2023 | 1 | 2023 |
| Self-Organizing Maps for Multi-system and Multi-view Datasets H Ishibashi, T Furukawa 2016 Joint 8th International Conference on Soft Computing and Intelligent …, 2016 | 1 | 2016 |
| End-condition for solution small angle X-ray scattering measurements by kernel density estimation H Sekiguchi, N Ohta, H Ishibashi, H Hino, M Mizumaki Science and Technology of Advanced Materials: Methods 2 (1), 426-434, 2022 | | 2022 |
| Low-rank kernel decomposition for scalable manifold modeling K Miyazaki, S Takano, R Tsuno, H Ishibashi, T Furukawa 2022 Joint 12th International Conference on Soft Computing and Intelligent …, 2022 | | 2022 |
| What is the true objective of multi-task manifold modeling?--Comparison of maximum likelihood and optimal transport approaches R Tsuno, H Ishibashi, T Furukawa IEICE Technical Report; IEICE Tech. Rep. 120 (403), 53-58, 2021 | | 2021 |
| Visualization tool for basketball team performance by multi-level SOM K Senoura, H Ishibashi, T Furukawa IEICE Technical Report; IEICE Tech. Rep. 119 (382), 27-31, 2020 | | 2020 |
| Dimensionality reduction method for gaussian process posteriors based on information geometry H Ishibashi, S Akaho IEICE Technical Report; IEICE Tech. Rep. 119 (360), 17-24, 2020 | | 2020 |
| Hierarchical Tensor Manifold Modeling for Multi-Group Analysis H Ishibashi, M Era, T Furukawa IEICE Transactions on Fundamentals of Electronics, Communications and …, 2018 | | 2018 |
| Multi-task manifold learning using hierarchical modeling for insufficient samples H Ishibashi, K Higa, T Furukawa Neural Information Processing: 25th International Conference, ICONIP 2018 …, 2018 | | 2018 |
| Visualization Method of Viewpoints Latent in a Dataset H Ishibashi Neural Information Processing: 25th International Conference, ICONIP 2018 …, 2018 | | 2018 |
| An attempt of continuous latent variable model by non-negative kernel smoother H Ishibashi, T Iwasaki, R Watanabe, T Furukawa IEICE Technical Report; IEICE Tech. Rep. 117 (325), 29-34, 2017 | | 2017 |
