| Selective inference for sparse high-order interaction models S Suzumura, K Nakagawa, Y Umezu, K Tsuda, I Takeuchi International Conference on Machine Learning, 3338-3347, 2017 | 45 | 2017 |
| Can AI predict animal movements? Filling gaps in animal trajectories using inverse reinforcement learning T Hirakawa, T Yamashita, T Tamaki, H Fujiyoshi, Y Umezu, I Takeuchi, ... Ecosphere 9 (10), e02447, 2018 | 44 | 2018 |
| Post selection inference with kernels M Yamada, Y Umezu, K Fukumizu, I Takeuchi International conference on artificial intelligence and statistics, 152-160, 2018 | 34 | 2018 |
| A novel sensitive detection method for DNA methylation in circulating free DNA of pancreatic cancer K Shinjo, K Hara, G Nagae, T Umeda, K Katsushima, M Suzuki, ... PLoS One 15 (6), e0233782, 2020 | 29 | 2020 |
| AIC for the non-concave penalized likelihood method Y Umezu, Y Shimizu, H Masuda, Y Ninomiya Annals of the Institute of Statistical Mathematics 71 (2), 247-274, 2019 | 26 | 2019 |
| Efficient learning algorithm for sparse subsequence pattern-based classification and applications to comparative animal trajectory data analysis T Sakuma, K Nishi, K Kishimoto, K Nakagawa, M Karasuyama, Y Umezu, ... Advanced Robotics 33 (3-4), 134-152, 2019 | 12 | 2019 |
| Selective inference for change point detection in multi-dimensional sequences Y Umezu, I Takeuchi arXiv preprint arXiv:1706.00514, 2017 | 12 | 2017 |
| Variable selection in multivariate linear models for functional data via sparse regularization H Matsui, Y Umezu Japanese Journal of Statistics and Data Science 3, 453-467, 2020 | 3 | 2020 |
| Post clustering inference for heterogeneous data S Inoue, Y Umezu, S Tsubota, I Takeuchi IEICE Technical Report; IEICE Tech. Rep. 117 (293), 69-76, 2017 | 3 | 2017 |
| Selective Inference for High-order Interaction Features Selected in a Stepwise Manner S Suzumura, K Nakagawa, Y Umezu, K Tsuda, I Takeuchi IPSJ Transactions on Bioinformatics 14, 1-11, 2021 | 1 | 2021 |
| Selective inference via marginal screening for high dimensional classification Y Umezu, I Takeuchi Japanese Journal of Statistics and Data Science 2 (2), 559-589, 2019 | 1 | 2019 |
| Selective Inference for Time-series Change-Point Analysis Y Umezu, K Nakagawa, S Inoue, K Tsuda, M Sugiyama, T Maekawa, ... IEICE Technical Report; IEICE Tech. Rep. 116 (209), 89-92, 2016 | 1 | 2016 |
| On the Consistency of the Bias Correction Term of the AIC for the Non-Concave Penalized Likelihood Method Y Umezu, Y Ninomiya arXiv preprint arXiv:1603.07843, 2016 | 1 | 2016 |
| Selective Inference in Propensity Score Analysis Y Ninomiya, Y Umezu, I Takeuchi arXiv preprint arXiv:2105.00416, 2021 | | 2021 |
| Selective Inference for Dynamic Programming-based Sequence Segmentation H Toda, Y Umezu, T Sakuma, I Takeuchi IEICE Technical Report; IEICE Tech. Rep. 118 (284), 279-286, 2018 | | 2018 |
| Selective Inference for Feature Selection after Hierarchical Clustering K Suzuki, S Inoue, Y Umezu, I Takeuchi IEICE Technical Report; IEICE Tech. Rep. 118 (284), 197-204, 2018 | | 2018 |
| Active Learning in Sparse Linear Regression Models via Selective Inference Y Umezu, I Takeuchi IEICE Technical Report; IEICE Tech. Rep. 118 (284), 381-388, 2018 | | 2018 |
| Selective Inference for Predictive Sequence Mining and Its Applications to Trajectory Data Analysis K Nishi, T Sakuma, Y Umezu, S Kajioka, I Takeuchi IEICE Technical Report; IEICE Tech. Rep. 118 (81), 23-29, 2018 | | 2018 |
| Post Clustering Inference, with Application to Single Cell Analysis S Inoue, Y Umezu, S Tsubota, I Takeuchi IEICE Technical Report; IEICE Tech. Rep. 118 (81), 15-22, 2018 | | 2018 |
| Selective Inference for Change Point Detection in Multidimensional Sequence Y Umezu, I Takeuchi IEICE Technical Report; IEICE Tech. Rep. 117 (293), 269-276, 2017 | | 2017 |
