Yu-Jie Zhang @ RIKEN AIP
About Me
I am a postdoctoral researcher in the
Imperfect Information Learning Team at
RIKEN AIP. I earned my Ph.D. from
the University of Tokyo under the supervision of
Prof. Masashi Sugiyama. Previously, I completed my M.Sc. with the LAMDA Group at
Nanjing University, under the supervision of
Prof. Zhi-Hua Zhou.
Research Interests
My research interests include Machine Learning and Data Mining. Currently, I am interested in
- Online Learning and Sequential Decision Making
- Machine Learning in Non-stationary Environments
Conference Papers
-
Generalized Linear Bandits: Almost Optimal Regret with One-Pass Update. [PDF, arXiv, bibtex]
Yu-Jie Zhang, Sheng-An Xu, Peng Zhao, and Masashi Sugiyama.
In: Advances in Neural Information Processing Systems 38 (NeurIPS 2025), San Diego, California, 2025. Page: to appear.
-
Recursive Reward Aggregation. [PDF, arXiv, bibtex]
Yuting Tang, Yivan Zhang, Johannes Ackermann, Yu-Jie Zhang, Soichiro Nishimori, Masashi Sugiyama.
Reinforcement Learning Conference 2025 (RLC 2025), Edmonton, Canada.
-
Non-stationary Online Learning for Curved Losses: Improved Dynamic Regret via Mixability. [PDF, arXiv, bibtex]
Yu-Jie Zhang, Peng Zhao, and Masashi Sugiyama.
In: Proceedings of the 42nd International Conference on Machine Learning (ICML 2025), Vancouver, Canada, 2025. Page: to appear.
-
Heavy-Tailed Linear Bandits: Huber Regression with One-Pass Update. [PDF, arXiv, bibtex]
Jing Wang, Yu-Jie Zhang, Peng Zhao, and Zhi-Hua Zhou.
In: Proceedings of the 42nd International Conference on Machine Learning (ICML 2025), Vancouver, Canada, 2025. Page: to appear.
-
Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation. [PDF, arXiv, bibtex]
Long-Fei Li, Yu-Jie Zhang, Peng Zhao, and Zhi-Hua Zhou.
In: Advances in Neural Information Processing Systems 37 (NeurIPS 2024), Vancouver, Canada, 2024. Page: 58539-58573.
-
Efficient Non-stationary Online Learning by Wavelets with Applications to Online Distribution Shift Adaptation. [PDF, bibtex]
Yu-Yang Qian, Peng Zhao, Yu-Jie Zhang, Masashi Sugiyama, Zhi-Hua Zhou.
In: Proceedings of the 41st International Conference on Machine Learning (ICML 2024), Vienna, Austria, 2024. Page: 41383-41415.
-
Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical. [PDF, bibtex]
Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, Masashi Sugiyama.
In: Proceedings of the 41st International Conference on Machine Learning (ICML 2024), Vienna, Austria. Page: 50683-50710.
-
Adapting to Continuous Covariate Shift via Online Density Ratio Estimation. [PDF, arXiv, bibtex]
Yu-Jie Zhang, Zhen-Yu Zhang, Peng Zhao, and Masashi Sugiyama.
In: Advances in Neural Information Processing Systems 36 (NeurIPS 2023), New Orleans, Louisiana, 2023. Page: 29074-29113.
-
Online (Multinomial) Logistic Bandit: Improved Regret and Constant Computation Cost. [PDF, bibtex] (Spotlight)
Yu-Jie Zhang and Masashi Sugiyama.
In: Advances in Neural Information Processing Systems 36 (NeurIPS 2023), New Orleans, Louisiana, 2023. Page: 29741-29782.
-
Imitation Learning from Vague Feedback. [PDF, bibtex]
Xin-Qiang Cai, Yu-Jie Zhang, Chao-Kai Chiang and Masashi Sugiyama.
In: Advances in Neural Information Processing Systems 36 (NeurIPS 2023), New Orleans, Louisiana, 2023. Page: 48275-48292.
-
Adapting to Online Label Shift with Provable Guarantees. [PDF, arXiv, code, bibtex]
Yong Bai*, Yu-Jie Zhang*, Peng Zhao, Masashi Sugiyama, and Zhi-Hua Zhou. (* indicates equal contribution)
In: Advances in Neural Information Processing Systems 35 (NeurIPS 2022), New Orleans, Louisiana, 2022. Page: 29960-29974.
-
Adaptive Learning for Weakly Labeled Streams. [PDF, code, bibtex]
Zhen-Yu Zhang, Yu-Yang Qian, Yu-Jie Zhang, Yuan Jiang, Zhi-Hua Zhou.
In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022), Washington, DC, 2022.
-
Towards Enabling Learnware to Handle Unseen Jobs. [PDF, code, bibtex]
Yu-Jie Zhang, Yu-Hu Yan, Peng Zhao and Zhi-Hua Zhou.
In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), online, 2021. Page: 10964-10972.
-
Exploratory Machine Learning with Unknown Unknowns.[PDF, code, bibtex]
Peng Zhao, Yu-Jie Zhang and Zhi-Hua Zhou.
In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), online, 2021. Page: 10999-11006.
-
An Unbiased Risk Estimator for Learning with Augmented Classes.[PDF, arXiv, code, bibtex]
Yu-Jie Zhang, Peng Zhao, Lanjihong Ma and Zhi-Hua Zhou.
In: Advances in Neural Information Processing Systems 33 (NeurIPS 2020), online, 2020. Page: 10247-10258.
-
Dynamic Regret of Convex and Smooth Functions.[PDF, arXiv, bibtex]
Peng Zhao, Yu-Jie Zhang, Lijun Zhang and Zhi-Hua Zhou.
In: Advances in Neural Information Processing Systems 33 (NeurIPS 2020), online, 2020. Page: 12510-12520.
-
A Simple Online Algorithm for Competing with Dynamic Comparators. [PDF, bibtex, slide]
Yu-Jie Zhang, Peng Zhao, and Zhi-Hua Zhou.
In: Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI 2020), online, 2020.
Journal Paper
-
Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization. [conference version, Journal version, arXiv, bibtex]
Sijia Chen, Yu-Jie Zhang, Wei-Wei Tu, Peng Zhao, and Lijun Zhang.
Journal of Machine Learning Research (JMLR), to appear, 2024.
-
Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization. [PDF, arXiv, bibtex]
Peng Zhao, Yu-Jie Zhang, Lijun Zhang, and Zhi-Hua Zhou.
Journal of Machine Learning Research (JMLR), 25(98):1−52, 2024.
-
Exploratory Machine Learning with Unknown Unknowns. [PDF, code, bibtex]
Peng Zhao, Jia-Wei Shan, Yu-Jie Zhang and Zhi-Hua Zhou.
Artificial Intelligence (AIJ), to appear, 2024.
Academic Service
- Reviewer for Conferences: AAAI (2021, 2024), AISTATS (2021-2024), ECAI (2020), ICLR (2022-2025), ICML (2022-2025), IJCAI (2020-2023), NeurIPS (2021-2025), UAI (2022-2024)
- Reviewer for Journals: JMLR, IEEE TPAMI, FCS
Awards & Honors
- Dean's Award for Outstanding Achievement (Doctoral Course), GSFS, UTokyo, 2025
- AISTATS 2025 Best Reviewer, 2025
- NeurIPS 2023 Top Reviewer, 2023
- UAI 2023 Top Reviewer, 2023
- NeurIPS 2022 Top Reviewer, 2022
- Todai Fellowship, 2021
- Excellent Graduate of Nanjing University, Nanjing, 2021
- National Graduate Scholarship for Master Student, MOE of PRC, 2020
- Excellent Graduate of Tongji University, Shanghai, 2018
- Shanghai Undergraduate Scholarship, Shanghai, 2017, 2016