Rchr
J-GLOBAL ID：202001014842103336
Update date: May. 10, 2024
Ishida Takashi
イシダ タカシ | Ishida Takashi
Affiliation and department：
Job title：
Research Scientist
Other affiliations (1)：
Research theme for competitive and other funds (4)：
- 2022 - 2027 機械学習の汎化性能と信頼性の向上に関する研究
- 2022 - 2026 弱教師付き学習による衛星画像からの3D土地被覆地図生成
- 2020 - 2023 ベイズ誤差推定及び正則化手法の研究
- 2020 - 2021 主観的ラベル付きデータに基づく機械学習の研究
Papers (9)：
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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
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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
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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
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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
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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
ｍore...
Books (1)：
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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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