I am a Research Scientist at RIKEN AIP, an Associate Professor at The University of Tokyo, and a part-time Research Scientist at Sakana AI.

At UTokyo, I am co-running Machine Learning and Statistical Data Analysis Lab (Sugiyama-Yokoya-Ishida Lab). I belong to the Department of Complexity Science and Engineering, the Department of Computer Science, and the Department of Information Science.

I earned my PhD from the University of Tokyo, advised by Prof. Masashi Sugiyama. During my PhD, I completed an Applied Scientist internship at Amazon.com and was fortunate to become a PhD Fellow at Google and a Research Fellow at JSPS (DC2). Prior to that, I spent some years in the finance industry, during which I worked as an Assistant Manager at Sumitomo Mitsui DS Asset Management. I received the MSc from the University of Tokyo and the Bachelor of Economics from Keio University.

Papers (peer reviewed)

1. W. Wang, T. Ishida, Y.-J. Zhang, G. Niu, M. Sugiyama. Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical. In Proceedings of the 41st International Conference on Machine Learning (ICML2024). [arXiv] [PMLR] [code]
   

2. T. Ishida, I. Yamane, N. Charoenphakdee, G. Niu, M. Sugiyama. Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classification. In Proceedings of Eleventh International Conference on Learning Representations (ICLR2023). [arXiv] [OpenReview] [code] [Fashion-MNIST-H (Papers with Code)] [Video] Selected for oral (notable-top-5%) presentation!
   

3. I. Yamane, Y. Chevaleyre, T. Ishida, F. Yger. Mediated Uncoupled Learning and Validation with Bregman Divergences: Loss Family with Maximal Generality. In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS2023). [paper] [code] [video]
   

4. Z. Lu, C. Xu, B. Du, T. Ishida, L. Zhang, & M. Sugiyama. LocalDrop: A hybrid regularization for deep neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.44, No.7, pp.3590-3601, 2022. [paper]
   

5. H. Ishiguro, T. Ishida, & M. Sugiyama. Learning from Noisy Complementary Labels with Robust Loss Functions. IEICE Transactions on Information and Systems, Vol.E105-D, No.2, pp.364-376, Feb. 2022. [paper]
   

6. T. Ishida, I. Yamane, T. Sakai, G. Niu, M. Sugiyama. Do We Need Zero Training Loss After Achieving Zero Training Error? In Proceedings of Thirty-seventh International Conference on Machine Learning (ICML2020). [paper] [code] [video]
   

7. T. Ishida, G. Niu, A. K. Menon, and M. Sugiyama. Complementary-label learning for arbitrary losses and models. In Proceedings of Thirty-sixth International Conference on Machine Learning (ICML2019). [paper] [poster] [slides] [video] [code]
   

8. T. Ishida, G. Niu, and M. Sugiyama. Binary classification from positive-confidence data. In Advances in Neural Information Processing Systems 31 (NeurIPS2018). [paper] [poster] [slides] [video] [code] [Press Release] [ScienceDaily] [PHYS.ORG] [ASIAN SCIENTISTS] [ISE Magazine] [RIKEN RESEARCH] [日刊工業新聞] [ITmedia] Selected for spotlight presentation!
   

9. T. Ishida, G. Niu, W. Hu, and M. Sugiyama. Learning from complementary labels. In Advances in Neural Information Processing Systems 30 (NeurIPS2017). [paper] [日刊工業新聞]

10. T. Ishida. Forecasting Nikkei 225 Returns By Using Internet Search Frequency Data. In Securities Analysts Journal, Vol.52, No.6, pp.83-93, 2014. Selected as Research Notes.

Books

1. M. Sugiyama, H. Bao, T. Ishida, N. Lu, T. Sakai, & G. Niu. Machine Learning from Weak Supervision: An Empirical Risk Minimization Approach. Adaptive Computation and Machine Learning series, The MIT Press, 2022. [link]
   

Grants

• Grant-in-Aid for Scientific Research (B), JSPS, 2022--2025 (Co-Investigator)

• Grant-in-Aid for Early-Career Scientists, JSPS, 2022--2026

• Frontier of mathematics and information science, ACT-X, JST, 2020--2023 Selected as one of the achievements (JP/EN) at JST in FY2024!

• Grant-in-Aid for JSPS Fellows, JSPS, 2020--2021

Awards & Achievements

• Asian Trustworthy Machine Learning (ATML) Fellowship, 2025

• Expert Reviewer, Transactions on Machine Learning Research (received in 2024)

• Achievements, Information and Communications Technology (JP/EN/PDF) at JST, 2024

• Funai Information Technology Award for Young Researchers, 2022 (received in 2023)

• IEICE TC-IBISML Research Award Finalist, 2020 (received in 2021)

• Dean's Award for Outstanding Achievement, Graduate School of Frontier Sciences 2021

• Toyota/Dwango AI Scholarship, 2020 – 2021

• Award Finalist, IBIS2020

• Top 10% reviewer, NeurIPS 2020

• JSPS Research Fellowship for Young Scientists (DC2), 2020 – 2021

• Top 50% reviewer, NeurIPS 2019

• Google PhD Fellowship 2019

• IEICE TC-IBISML Research Award, 2017 (received in 2018)

Service

• Committee member: FY2022 -- FY2023 IEICE, Information-Based Induction Sciences and Machine Learning (IBISML) Technical Group

• Workshop organizer: PC member, IBIS2023. Executive Group, TrustML Young Scientist Seminars. Organizer, NeurIPS Meetup Japan 2021.

• Area chair: [2025] ICLR, ICML, ACML

• Conference PC/reviewer: [2025] NeurIPS [2024] ICLR, AISTATS, ICML, ACML, NeurIPS [2023] ICLR [2022] ICLR, AISTATS, ICML, NeurIPS, [2021] NeurIPS, ACML, ICLR, UAI, ICML, [2020] NeurIPS (top 10% reviewer), ICML, ICLR, AAAI, AISTATS, UAI, ACML, [2019] NeurIPS (top 50% reviewer), ICML, AAAI, AISTATS, UAI, ACML

• Journal action editor: Transactions on Machine Learning Research (TMLR; since 2024)

• Journal reviewer: IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, Journal of Information Processing, Machine Learning, Artificial Intelligence (AIJ), Transactions on Machine Learning Research (TMLR; Expert Reviewer in 2024)

• Workshop reviewer: 3rd edition of Reproducibility Challenge @ NeurIPS 2019, IJCAI 2021 Workshop on Weakly Supervised Representation Learning

Courses at UTokyo

• Advanced Data Analysis (Graduate) with Prof. Masashi Sugiyama: 2021 S1S2 (Japanese), 2023 S1S2 (English), 2025 S1S2 (English)

• Statistical Machine Learning (Undergraduate) with Prof. Issei Sato and Prof. Masashi Sugiyama (in Japanese): 2021 S1S2, 2022 S1S2, 2023 S1S2, 2024 S1S2, 2025 S1S2

• Statistics and Optimization (Undergraduate) with Prof. Issei Sato and Prof. Masashi Sugiyama (in Japanese): 2021 A1A2, 2022 A1A2, 2023 A1A2, 2024 A1A2

• Intelligent Systems (Undergraduate) with Prof. Issei Sato, Prof. Masashi Sugiyama, and Prof. Yusuke Miyao (in Japanese): 2021 A1A2, 2022 A1A2, 2023 A1A2, 2024 A1A2

• Machine Learning: 2024 Spring (Japanese, in UTokyo Extension)

For Prospective Students

I welcome motivated students interested in machine learning, LLMs, and related fields to join my group. Please check my recent publications to get a sense of my research interests. I can supervise or mentor students through the following programs:

• Graduate students (Master's & PhD): I supervise Master's and PhD students in Department of Complexity Science and Engineering (Kashiwa Campus) and Department of Computer Science (Hongo Campus) at the University of Tokyo. If you are interested in applying, please review the official guidelines at the links above. If you are interested in the PhD program, feel free to contact me with your CV and a 2-3 page research plan.
• 4th year undergraduates: I supervise undergraduate thesis students from the Department of Information Science at the University of Tokyo.
• 1st-3rd year undergraduates: I cannot supervise, but I may have the chance to mentor students in Japanese universities through the RIKEN-AIP Undergraduate Research Program (PI: Prof. Masashi Sugiyama, Imperfect Information Learning Team, Nihonbashi Office). See Japanese official guide.

If you have any questions, feel free to contact me.

Contact & Links

• Email: ishi at k.u-tokyo dot ac dot jp
• Links: Github, X, Instagram, YouTube, Google Scholar, researchmap