石田隆

イシダタカシ | Ishida Takashi

所属機関・部署：
職名：研究員
その他の所属（所属・部署名・職名） (1件)：

競争的資金等の研究課題 (4件)：

論文 (9件)：

Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama. Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical. Proceedings of the Forty-first International Conference on Machine Learning (ICML 2024). 2024
Ikko Yamane, Yann Chevaleyre, Takashi Ishida, Florian Yger. Mediated Uncoupled Learning and Validation with Bregman Divergences: Loss Family with Maximal Generality. Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023). 2023
Takashi Ishida, Ikko Yamane, Nontawat Charoenphakdee, Gang Niu, Masashi Sugiyama. Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. Proceedings of Eleventh International Conference on Learning Representations (ICLR 2023). 2023
Hiroki Ishiguro, Takashi Ishida, Masashi Sugiyama. Learning from Noisy Complementary Labels with Robust Loss Functions. IEICE Transactions on Information and Systems. 2022. E105.D. 2. 364-376
Ziqing Lu, Chang Xu, Bo Du, Takashi Ishida, Lefei Zhang, Masashi Sugiyama. LocalDrop: A Hybrid Regularization for Deep Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2022. 44. 7. 1-1

書籍 (1件)：

Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach (Adaptive Computation and Machine Learning series)
The MIT Press 2022 ISBN:0262047071

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石田 隆