| Learning from complementary labels T Ishida, G Niu, W Hu, M Sugiyama Advances in neural information processing systems (NeurIPS 2017), 2017 | 239 | 2017 |
| 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 | 195 | 2020 |
| Complementary-label learning for arbitrary losses and models T Ishida, G Niu, AK Menon, M Sugiyama International Conference on Machine Learning (ICML 2019), 2019 | 148 | 2019 |
| Binary classification from positive-confidence data T Ishida, G Niu, M Sugiyama Advances in neural information processing systems (NeurIPS 2018), 2018 | 98 | 2018 |
| 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 | 47 | 2022 |
| 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 | 26 | 2021 |
| 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 | 22 | 2023 |
| 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 | 12 | 2022 |
| 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 | 9* | 2024 |
| 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 | 3 | 2023 |
| Importance Weighting for Aligning Language Models under Deployment Distribution Shift T Lodkaew, T Fang, T Ishida, M Sugiyama Transactions on Machine Learning Research (TMLR), 2025 | 2 | 2025 |
| EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements I Sugiura, T Ishida, T Makino, C Tazuke, T Nakagawa, K Nakago, D Ha arXiv preprint arXiv:2506.08762, 2025 | 2 | 2025 |
| Flooding regularization for stable training of generative adversarial networks I Yahiro, T Ishida, N Yokoya arXiv preprint arXiv:2311.00318, 2023 | 2 | 2023 |
| Scalable Oversight via Partitioned Human Supervision R Yin, T Ishida, M Sugiyama arXiv preprint arXiv:2510.22500, 2025 | | 2025 |
| LLM Routing with Dueling Feedback CK Chiang, T Ishida, M Sugiyama arXiv preprint arXiv:2510.00841, 2025 | | 2025 |
| Off-Policy Corrected Reward Modeling for Reinforcement Learning from Human Feedback J Ackermann, T Ishida, M Sugiyama Conference on Language Modeling (COLM 2025), 2025 | | 2025 |
| Practical estimation of the optimal classification error with soft labels and calibration R Ushio, T Ishida, M Sugiyama arXiv preprint arXiv:2505.20761, 2025 | | 2025 |
| How Can I Publish My LLM Benchmark Without Giving the True Answers Away? T Ishida, T Lodkaew, I Yamane ICML 2025 Workshop on the Impact of Memorization on Trustworthy Foundation …, 2025 | | 2025 |
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
