Yu-Jie Zhang
The University of Tokyoyujie.zhang@ms.k.u-tokyo.ac.jp
yujie-zhang96.github.io

EDUCATIONThe University of Tokyo, Japan
Ph.D. candidate, Complexity Science and Engineering

October 2021 - Present
Supervisor: Prof. Masashi Sugiyama

Nanjing University, China
M.Sc., Computer Science and Technology

June 16, 2021
Supervisor: Prof. Zhi-Hua Zhou

Tongji University, China
B.Sc., Electronic Science and Technology

July 01, 2018
GPA: 4.91/5.00, ranking 1/32

RESEARCH INTEREST
I am generally interested in exploring the theoretical foundations of machine learning and developing
methods with sound theoretical guarantees. Currently, I focus on developing provably adaptive andreliable methods for non-stationary and open-world environments. My research topics include:• Non-stationary Online Learning and Decision-making: We develop online learning methods
that adapt promptly to non-stationary environment with dynamic regret guarantees- Key Words: online optimization, bandits, reinforcement learning, dynamic regret bound.
• Learning in Open-world Environments: We develop reliable methods to learn with weak supervision and handle unknown classes with excess risk guarantees.
- Key Words: distribution shift, weakly supervised learning, unknown classes, excess risk bound.
PUBLICATIONSPreprints[1] Long-Fei Li, Yu-Jie Zhang, Peng Zhao, and Zhi-Hua Zhou. Provably Eﬀicient Reinforcement Learning with Multinomial Logit Function Approximation.
Conference Publications[2] Yu-Yang Qian, Peng Zhao, Yu-Jie Zhang, Masashi Sugiyama, Zhi-Hua Zhou. Eﬀicient Nonstationary Online Learning by Wavelets with Applications to Online Distribution Shift Adaptation.
In: Proceedings of the 41st International Conference on Machine Learning (ICML), 2024.
[3] Wei Wang, Takashi Ishida, Yu-Jie Zhang, Gang Niu, and Masashi Sugiyama. Learning with Complementary Labels Revisited: A Consistent Approach via Negative-Unlabeled Learning. In: Proceedingsof the 41st International Conference on Machine Learning (ICML), 2024.
[4] Yu-Jie Zhang and Masashi Sugiyama. Online (Multinomial) Logistic Bandit: Improved Regret andConstant Computation Cost. In Advances in Neural Information Processing Systems 36 (NeurIPS),
2023. [Spotlight][5] Yu-Jie Zhang, Zhen-Yu Zhang, Peng Zhao, and Masashi Sugiyama. Adapting to Continuous Covariate Shift via Online Density Ratio Estimation. In Advances in Neural Information ProcessingSystems 36 (NeurIPS), 2023.
[6] Xin-Qiang Cai, Yu-Jie Zhang, Chao-Kai Chiang and Masashi Sugiyama. Imitation Learning fromVague Feedback. In Advances in Neural Information Processing Systems 36 (NeurIPS), 2023.

[7] Yong Bai*, Yu-Jie Zhang*, Peng Zhao, Masashi Sugiyama, and Zhi-Hua Zhou. Adapting to Online
Label Shift with Provable Guarantees. In Advances in Neural Information Processing Systems 35(NeurIPS), 2022. (* equal contribution)[8] Zhen-Yu Zhang, Yu-Yang Qian, Yu-Jie Zhang, Yuan Jiang, Zhi-Hua Zhou. Adaptive Learningfor Weakly Labeled Streams. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge
Discovery and Data Mining (KDD), 2022.
[9] Yu-Jie Zhang, Yu-Hu Yan, Peng Zhao and Zhi-Hua Zhou. Towards Enabling Learnware to Handle
Unseen Jobs. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021.
[10] Peng Zhao, Yu-Jie Zhang and Zhi-Hua Zhou. Exploratory Machine Learning with Unknown Unknowns. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021.
[11] Yu-Jie Zhang, Peng Zhao, Lanjihong Ma and Zhi-Hua Zhou. An Unbiased Risk Estimator for Learning with Augmented Classes. In Advances in Neural Information Processing Systems 33 (NeurIPS),2020.
[12] Peng Zhao, Yu-Jie Zhang, Lijun Zhang and Zhi-Hua Zhou. Dynamic Regret of Convex and Smooth
Functions. In Advances in Neural Information Processing Systems 33 (NeurIPS), 2020.
[13] Yu-Jie Zhang, Peng Zhao, and Zhi-Hua Zhou. A Simple Online Algorithm for Competing withDynamic Comparators. In Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence(UAI), 2020.
Journal Publications[14] Sijia Chen, Yu-Jie Zhang, Wei-Wei Tu, Peng Zhao, and Lijun Zhang. Optimistic Online Mirror
Descent for Bridging Stochastic and Adversarial Online Convex Optimization. Journal of Machine
Learning Research (JMLR), to appear, 2024.
[15] Peng Zhao, Yu-Jie Zhang, Lijun Zhang, and Zhi-Hua Zhou. Adaptivity and Non-stationarity:
Problem-dependent Dynamic Regret for Online Convex Optimization. Journal of Machine Learning
Research (JMLR), 25(98):1−52, 2024.
[16] Peng Zhao, Jia-Wei Shan, Yu-Jie Zhang and Zhi-Hua Zhou. Exploratory Machine Learning with
Unknown Unknowns. Artificial Intelligence (AIJ), 327:104059, 2024.
AWARDS & HONORS• Top Reviewer for NeurIPS, 2023
• Top Reviewer for UAI, 2023
• Top Reviewer for NeurIPS, 2022
• The University of Tokyo Fellowship, Tokyo, 2021
• Outstanding Master Dissertation Award by Jiangsu Computer Society, Nanjing, 2021
• Excellent Graduate of Nanjing University, Nanjing, 2021
• National Graduate Scholarship for Master Student, MOE of PRC, 2020
ACADEMIC SERVICE• Reviewer for Conference: NeurIPS (2021-2024), ICML (2022-2024), ICLR (2022-2024), AISTATS(2021-2024), UAI (2022-2024), AAAI (2021, 2024), IJCAI (2020-2023), ECAI (2020).
• Reviewer for Journal: Journal of Machine Learning Research (JMLR), IEEE Transactions on
Pattern Analysis and Machine Intelligence (TPAMI), Frontiers of Computer Science.