| Mean-variance analysis in Bayesian optimization under uncertainty S Iwazaki, Y Inatsu, I Takeuchi International Conference on Artificial Intelligence and Statistics, 973-981, 2021 | 29 | 2021 |
| Computing valid p-values for image segmentation by selective inference K Tanizaki, N Hashimoto, Y Inatsu, H Hontani, I Takeuchi Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 27 | 2020 |
| Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design K Inoue, M Karasuyama, R Nakamura, M Konno, D Yamada, K Mannen, ... Communications biology 4 (1), 362, 2021 | 22 | 2021 |
| Conditional selective inference for robust regression and outlier detection using piecewise-linear homotopy continuation T Tsukurimichi, Y Inatsu, VNL Duy, I Takeuchi Annals of the Institute of Statistical Mathematics 74 (6), 1197-1228, 2022 | 14 | 2022 |
| Active learning for distributionally robust level-set estimation Y Inatsu, S Iwazaki, I Takeuchi International Conference on Machine Learning, 4574-4584, 2021 | 13 | 2021 |
| Bayesian optimization for distributionally robust chance-constrained problem Y Inatsu, S Takeno, M Karasuyama, I Takeuchi International Conference on Machine Learning, 9602-9621, 2022 | 12 | 2022 |
| Bayesian optimization for cascade-type multistage processes S Kusakawa, S Takeno, Y Inatsu, K Kutsukake, S Iwazaki, T Nakano, ... Neural Computation 34 (12), 2408-2431, 2022 | 11 | 2022 |
| Bayesian experimental design for finding reliable level set under input uncertainty S Iwazaki, Y Inatsu, I Takeuchi IEEE Access 8, 203982-203993, 2020 | 11 | 2020 |
| Model selection criterion based on the prediction mean squared error in generalized estimating equations Y Inatsu, S Imori Hiroshima Mathematical Journal 48 (3), 307-334, 2018 | 11 | 2018 |
| Valid and exact statistical inference for multi-dimensional multiple change-points by selective inference R Sugiyama, H Toda, VNL Duy, Y Inatsu, I Takeuchi arXiv preprint arXiv:2110.08989, 2021 | 10 | 2021 |
| Randomized Gaussian process upper confidence bound with tighter Bayesian regret bounds S Takeno, Y Inatsu, M Karasuyama International Conference on Machine Learning, 33490-33515, 2023 | 7 | 2023 |
| Active learning for level set estimation under input uncertainty and its extensions Y Inatsu, M Karasuyama, K Inoue, I Takeuchi Neural Computation 32 (12), 2486-2531, 2020 | 7 | 2020 |
| Bayesian quadrature optimization for probability threshold robustness measure S Iwazaki, Y Inatsu, I Takeuchi Neural Computation 33 (12), 3413-3466, 2021 | 5 | 2021 |
| Active learning for enumerating local minima based on Gaussian process derivatives Y Inatsu, D Sugita, K Toyoura, I Takeuchi Neural Computation 32 (10), 2032-2068, 2020 | 5 | 2020 |
| Active learning for level set estimation under cost-dependent input uncertainty Y Inatsu, M Karasuyama, K Inoue, I Takeuchi arXiv preprint arXiv:1909.06064, 2019 | 5 | 2019 |
| Bayesian quadrature optimization for probability threshold robustness measure S Iwazaki, Y Inatsu, I Takeuchi arXiv preprint arXiv:2006.11986, 2020 | 4 | 2020 |
| Active learning of Bayesian linear models with high-dimensional binary features by parameter confidence-region estimation Y Inatsu, M Karasuyama, K Inoue, H Kandori, I Takeuchi Neural Computation 32 (10), 1998-2031, 2020 | 3 | 2020 |
| Bayesian experimental design for finding reliable level set under input uncertainty S Iwazaki, Y Inatsu, I Takeuchi arXiv preprint arXiv:1910.12043, 2019 | 3 | 2019 |
| Akaike information criterion for ANOVA model with a simple order restriction Y Inatsu TR 16-13, Statistical Research Group, Hiroshima University, Hiroshima, 2016 | 2 | 2016 |
| Active learning for identifying local minimum points based on the derivative of Gaussian process Y Inatsu, D Sugita, K Toyoura, I Takeuchi IEICE Technical Report; IEICE Tech. Rep. 118 (284), 373-380, 2018 | 1 | 2018 |
