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Takashi Ishida
Takashi Ishida
Research Scientist, RIKEN AIP / Lecturer, The University of Tokyo
Verified email at ms.k.u-tokyo.ac.jp - Homepage
Title
Cited by
Cited by
Year
Learning from complementary labels
T Ishida, G Niu, W Hu, M Sugiyama
Advances in neural information processing systems (NeurIPS 2017), 2017
1712017
Do We Need Zero Training Loss After Achieving Zero Training Error?
T Ishida, I Yamane, T Sakai, G Niu, M Sugiyama
International Conference on Machine Learning (ICML 2020), 2020
1382020
Complementary-label learning for arbitrary losses and models
T Ishida, G Niu, AK Menon, M Sugiyama
International Conference on Machine Learning (ICML 2019), 2019
972019
Binary classification from positive-confidence data
T Ishida, G Niu, M Sugiyama
Advances in neural information processing systems (NeurIPS 2018), 2018
712018
Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach
M Sugiyama, H Bao, T Ishida, N Lu, T Sakai, G Niu
MIT Press, 2022
222022
LocalDrop: A hybrid regularization for deep neural networks
Z Lu, C Xu, B Du, T Ishida, L Zhang, M Sugiyama
IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (7), 3590-3601, 2021
192021
Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification
T Ishida, I Yamane, N Charoenphakdee, G Niu, M Sugiyama
The Eleventh International Conference on Learning Representations (ICLR 2023), 2023
82023
Learning from Noisy Complementary Labels with Robust Loss Functions
H ISHIGURO, T ISHIDA, M SUGIYAMA
IEICE TRANSACTIONS on Information and Systems 105 (2), 364-376, 2022
72022
Mediated Uncoupled Learning and Validation with Bregman Divergences: Loss Family with Maximal Generality
I Yamane, Y Chevaleyre, T Ishida, F Yger
International Conference on Artificial Intelligence and Statistics (AISTATS …, 2023
12023
Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical
W Wang, T Ishida, YJ Zhang, G Niu, M Sugiyama
International Conference on Machine Learning (ICML 2024), 2024
2024
Flooding Regularization for Stable Training of Generative Adversarial Networks
I Yahiro, T Ishida, N Yokoya
arXiv preprint arXiv:2311.00318, 2023
2023
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