| Information geometry of U-Boost and Bregman divergence N Murata, T Takenouchi, T Kanamori, S Eguchi Neural Computation 16 (7), 1437-1481, 2004 | 238 | 2004 |
| Robustifying AdaBoost by adding the naive error rate T Takenouchi, S Eguchi Neural Computation 16 (4), 767-787, 2004 | 88 | 2004 |
| Parameter estimation for von Mises–Fisher distributions A Tanabe, K Fukumizu, S Oba, T Takenouchi, S Ishii Computational Statistics 22, 145-157, 2007 | 66 | 2007 |
| Robust loss functions for boosting T Kanamori, T Takenouchi, S Eguchi, N Murata Neural computation 19 (8), 2183-2244, 2007 | 49 | 2007 |
| Improving imbalanced classification using near-miss instances A Tanimoto, S Yamada, T Takenouchi, M Sugiyama, H Kashima Expert Systems with Applications 201, 117130, 2022 | 34 | 2022 |
| Self-measuring similarity for multi-task gaussian process K Hayashi, T Takenouchi, R Tomioka, H Kashima Proceedings of ICML Workshop on Unsupervised and Transfer Learning, 145-153, 2012 | 32 | 2012 |
| An extension of the receiver operating characteristic curve and AUC-optimal classification T Takenouchi, O Komori, S Eguchi Neural computation 24 (10), 2789-2824, 2012 | 28 | 2012 |
| Exponential family tensor factorization for missing-values prediction and anomaly detection K Hayashi, T Takenouchi, T Shibata, Y Kamiya, D Kato, K Kunieda, ... 2010 IEEE International Conference on Data Mining, 216-225, 2010 | 28 | 2010 |
| Robust boosting algorithm against mislabeling in multiclass problems T Takenouchi, S Eguchi, N Murata, T Kanamori Neural computation 20 (6), 1596-1630, 2008 | 28 | 2008 |
| The most robust loss function for boosting T Kanamori, T Takenouchi, S Eguchi, N Murata Neural Information Processing: 11th International Conference, ICONIP 2004 …, 2004 | 18 | 2004 |
| A unified statistically efficient estimation framework for unnormalized models M Uehara, T Kanamori, T Takenouchi, T Matsuda International Conference on Artificial Intelligence and Statistics, 809-819, 2020 | 17 | 2020 |
| Binary classifiers ensemble based on Bregman divergence for multi-class classification T Takenouchi, S Ishii Neurocomputing 273, 424-434, 2018 | 15 | 2018 |
| Regret minimization for causal inference on large treatment space A Tanimoto, T Sakai, T Takenouchi, H Kashima International Conference on Artificial Intelligence and Statistics, 946-954, 2021 | 14 | 2021 |
| Zero-shot domain adaptation based on attribute information M Ishii, T Takenouchi, M Sugiyama Asian Conference on Machine Learning, 473-488, 2019 | 13 | 2019 |
| Statistical inference with unnormalized discrete models and localized homogeneous divergences T Takenouchi, T Kanamori Journal of Machine Learning Research 18 (56), 1-26, 2017 | 11 | 2017 |
| Exponential family tensor factorization: an online extension and applications K Hayashi, T Takenouchi, T Shibata, Y Kamiya, D Kato, K Kunieda, ... Knowledge and information systems 33, 57-88, 2012 | 11 | 2012 |
| Multiclass classification as a decoding problem T Takenouchi, S Ishii 2007 IEEE Symposium on Foundations of Computational Intelligence, 470-475, 2007 | 11 | 2007 |
| Partially zero-shot domain adaptation from incomplete target data with missing classes M Ishii, T Takenouchi, M Sugiyama Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2020 | 10 | 2020 |
| Empirical localization of homogeneous divergences on discrete sample spaces T Takenouchi, T Kanamori Advances in Neural Information Processing Systems 28, 2015 | 10 | 2015 |
| Improving Logitboost with prior knowledge T Kanamori, T Takenouchi Information Fusion 14 (2), 208-219, 2013 | 9 | 2013 |
Shinto EguchiInstitute of Statistical MathematicsVerified email at ism.ac.jp
Shinto EguchiInstitute of Statistical MathematicsVerified email at ism.ac.jp