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ORCIDTakashi Ishida 0001
Person information
- affiliation: University of Tokyo, Japan
Other persons with the same name
- Takashi Ishida 0002
![[0000-0002-9478-3223]](https://dblp.org/img/orcid-mark.12x12.png "0000-0002-9478-3223")
— Tokyo Institute of Technology, Japan - Takashi Ishida 0003 — ROHM Semiconductor, Yokohama, Japan
- Takashi Ishida 0004 — Takasaki City University of Economics, Takasaki, Gunma, Japan
- Takashi Ishida 0005 — Hokkaido University, Sapporo, Japan
- Takashi Ishida 0006 — Toshiba-Mitsubishi-Electric Industrial Systems Corporation, Tokyo, Japan (and 1 more)
- Takashi Ishida 0007 — University of the Ryukyus, Okinawa, Japan
- Takashi Ishida 0008 — Nittetsu Plant Designing Corp., Japan
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2020 – today
- 2024
[c7]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. ICML 2024- 2023
- [c6]Ikko Yamane, Yann Chevaleyre, Takashi Ishida, Florian Yger:
Mediated Uncoupled Learning and Validation with Bregman Divergences: Loss Family with Maximal Generality. AISTATS 2023: 4768-4801 - [c5]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. ICLR 2023 - [i8]Iu Yahiro, Takashi Ishida, Naoto Yokoya:
Flooding Regularization for Stable Training of Generative Adversarial Networks. CoRR abs/2311.00318 (2023) - [i7]Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama:
Learning with Complementary Labels Revisited: A Consistent Approach via Negative-Unlabeled Learning. CoRR abs/2311.15502 (2023) - 2022
- [j2]Hiroki Ishiguro, Takashi Ishida, Masashi Sugiyama:
Learning from Noisy Complementary Labels with Robust Loss Functions. IEICE Trans. Inf. Syst. 105-D(2): 364-376 (2022) - [j1]Ziqing Lu, Chang Xu, Bo Du, Takashi Ishida, Lefei Zhang, Masashi Sugiyama:
LocalDrop: A Hybrid Regularization for Deep Neural Networks. IEEE Trans. Pattern Anal. Mach. Intell. 44(7): 3590-3601 (2022) - [i6]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. CoRR abs/2202.00395 (2022) - 2021
- [i5]Ziqing Lu, Chang Xu, Bo Du, Takashi Ishida, Lefei Zhang, Masashi Sugiyama:
LocalDrop: A Hybrid Regularization for Deep Neural Networks. CoRR abs/2103.00719 (2021) - 2020
- [c4]Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Do We Need Zero Training Loss After Achieving Zero Training Error? ICML 2020: 4604-4614 - [i4]Takashi Ishida, Ikko Yamane, Tomoya Sakai, Gang Niu, Masashi Sugiyama:
Do We Need Zero Training Loss After Achieving Zero Training Error? CoRR abs/2002.08709 (2020)
2010 – 2019
- 2019
- [c3]Takashi Ishida, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama:
Complementary-Label Learning for Arbitrary Losses and Models. ICML 2019: 2971-2980 - 2018
- [c2]Takashi Ishida, Gang Niu, Masashi Sugiyama:
Binary Classification from Positive-Confidence Data. NeurIPS 2018: 5921-5932 - [i3]Takashi Ishida, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama:
Complementary-Label Learning for Arbitrary Losses and Models. CoRR abs/1810.04327 (2018) - 2017
- [c1]Takashi Ishida, Gang Niu, Weihua Hu, Masashi Sugiyama:
Learning from Complementary Labels. NIPS 2017: 5639-5649 - [i2]Takashi Ishida, Gang Niu, Masashi Sugiyama:
Learning from Complementary Labels. CoRR abs/1705.07541 (2017) - [i1]Takashi Ishida, Gang Niu, Masashi Sugiyama:
Binary Classification from Positive-Confidence Data. CoRR abs/1710.07138 (2017)
Coauthor Index
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