个人简历

I'm a Ph.D. student of Department of Computer Science and Technology in Nanjing University and a member of LAMDA Group. I got my B.Sc. degree in Electronic Science and Technology in June 2016 from Tongji University. In the same year, I was admitted to pursue for a Ph.D. degree in Nanjing University.
Advisor
Professor Zhi-Hua Zhou.
Preprints
Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization. [PDF, arXiv]
Peng Zhao, Yu-Jie Zhang, Lijun Zhang, and Zhi-Hua Zhou.
No-Regret Learning in Time-Varying Zero-Sum Games. [PDF, arXiv]
Mengxiao Zhang*, Peng Zhao*, Haipeng Luo, and Zhi-Hua Zhou.
Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits. [PDF, arXiv]
Haipeng Luo, Mengxiao Zhang, Peng Zhao, and Zhi-Hua Zhou. (alphabetical order)
Adaptive Bandit Convex Optimization with Heterogeneous Curvature. [PDF, arXiv]
Haipeng Luo, Mengxiao Zhang, and Peng Zhao. (alphabetical order)
Technical Notes
Non-stationary Linear Bandits Revisited. [PDF, arXiv] Peng Zhao and Lijun Zhang. Technical Note, 2021.
Teaching Assistant
Introduction to Machine Learning Theory. (With Assoc. Prof. Wei Gao, Wei Wang, Lijun Zhang; For Graduate Students, Spring, 2019)
Introduction to Machine Learning. (With Prof. Zhi-Hua Zhou; For Undergraduate Students, Spring, 2018)
Digital Image Processing. (With Assoc. Prof. Wei Wang; For Undergraduate Students, Spring, 2018)
LAMDA Machine Learning Summer Seminar. (For New Students in LAMDA, Summer, 2017)
Introduction to Machine Learning. (With Prof. Zhi-Hua Zhou; For Undergraduate Students, Spring, 2017)
Combinatorics. (With Prof. Yitong Yin; For Graduate Students, Fall, 2016)
Awards & Honors
NeurIPS 2021 Outstanding Reviewer Award, 2021
IJCAI 2021 Distinguished Senior Program Committee Member, 2021
Baidu Scholarship, Baidu Inc, 2019
AAAI-19 Contribution Award as Workflow Chair, AAAI, 2019
National Scholarship for Doctoral Students, MOE of PRC, 2019
Presidential Special Scholarship for first-year Ph.D. Students in Nanjing University, Nanjing, 2016
Bao Gang Education Scholarship for Undergraduate, Shanghai, 2015
National Scholarship for Undergraduates, MOE of PRC, 2014, 2013
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研究领域

My research interests include topics in machine learning and data mining.
Machine Learning in Non-stationary Environments
Online Learning and Decision Making
Weakly Supervised Learning
Most recently, I am interested in""

近期论文

Conference Papers
Non-stationary Online Learning with Memory and Non-stochastic Control. [PDF, arXiv]Peng Zhao, Yu-Xiang Wang, and Zhi-Hua Zhou.In: Proceedings of the 25th International Conference on Artificial Intelligence and Statistics(AISTATS 2022), online, 2022. To appear.
Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits. [arXiv]Youming Tao, Yulian Wu, Peng Zhao, and Di Wang.In: Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022), online, 2022. To appear.
Improved Analysis for Dynamic Regret of Strongly Convex and Smooth Functions. [PDF, arXiv, bibtex] Peng Zhao and Lijun Zhang.In: Proceedings of the 3rd Conference on Learning for Dynamics and Control (L4DC 2021), online, 2021. Page: 48-59.
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.
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.
Storage Fit Learning with Feature Evolvable Streams. [PDF, code, bibtex]
Bo-Jian Hou, Yu-Hu Yan, Peng Zhao, and Zhi-Hua Zhou.In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), online, 2021. Page: 7729-7736.
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), Vancouver, Canada, 2020. Page: 12510-12520.
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), Vancouver, Canada, 2020. Page: 10247-10258.
Learning with Feature and Distribution Evolvable Streams. [PDF, code, bibtex]Zhen-Yu Zhang, Peng Zhao, Yuan Jiang, and Zhi-Hua Zhou.In: Proceedings of the 37th International Conference on Machine Learning (ICML 2020), Vienna, Austria, 2020. Page: 11317-11327.
A Simple Online Algorithm for Competing with Dynamic Comparators. [PDF, bibtex]Yu-Jie Zhang, Peng Zhao, and Zhi-Hua Zhou. In: Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI 2020), Toronto, Canada, 2020. Page: 390-399.
Bandit Convex Optimization in Non-stationary Environments. [PDF, journal, arXiv, bibtex] Peng Zhao, Guanghui Wang, Lijun Zhang, and Zhi-Hua Zhou.
In: Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), Palermo, Italy, 2020. Page: 1508-1518.
A Simple Approach for Non-stationary Linear Bandits. [PDF, arXiv, errata, bibtex] Peng Zhao, Lijun Zhang, Yuan Jiang, and Zhi-Hua Zhou. In: Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), Palermo, Italy, 2020. Page: 746-755.
Optimal Margin Distribution Learning in Dynamic Environments. [PDF, bibtex] Teng Zhang, Peng Zhao, and Hai Jin. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI 2020), New York, NY, 2020. Page: 6821-6828.
Nearest Neighbor Ensembles: An Effective Method for Difficult Problems in Streaming Classification with Emerging New Classes. [PDF, code, bibtex] Xin-Qiang Cai, Peng Zhao, Kai Ming Ting, Xin Mu, and Yuan Jiang. In: Proceedings of the 19th International Conference on Data Mining (ICDM 2019), Beijing, China, 2019. Page: 970-975.
Learning from Incomplete and Inaccurate Supervision. [PDF, code, bibtex] Zhen-Yu Zhang, Peng Zhao, Yuan Jiang, and Zhi-Hua Zhou. In: Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), Anchorage, AL, 2019. Page: 1017-1025.
Improving Deep Forest by Confidence Screening. [PDF, code, bibtex] Ming Pang, Kai Ming Ting, Peng Zhao, and Zhi-Hua Zhou.In: Proceedings of the 18th IEEE International Conference on Data Mining (ICDM 2018), Singapore, 2018. Page: 1194-1199.
Label Distribution Learning by Optimal Transport. [PDF, supp, code, bibtex]Peng Zhao, Zhi-Hua Zhou.In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI 2018), New Orleans, Louisiana, 2018. Page: 4506-4513.
Dual Set Multi-Label Learning. [PDF, supp, code, bibtex]Chong Liu, Peng Zhao, Sheng-Jun Huang, Yuan Jiang, and Zhi-Hua Zhou.In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI 2018), New Orleans, Louisiana, 2018. Page: 3635-3642.
Multi-View Matrix Completion for Clustering with Side Information. [PDF, code, bibtex] Peng Zhao, Yuan Jiang, and Zhi-Hua Zhou. In: Proceedings of the 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017), LNCS, Jeju, Korea, 2017. Page:403-415.
Journal Papers
Bandit Convex Optimization in Non-stationary Environments. [PDF, arXiv, bibtex] Peng Zhao, Guanghui Wang, Lijun Zhang, and Zhi-Hua Zhou. Journal of Machine Learning Research (JMLR), 22(125):1−45, 2021.
基于决策树模型重用的分布变化流数据学习. [PDF] 赵鹏, 周志华. 中国科学：信息科学, 2021, 51(1): 1-12.
Learning from Incomplete and Inaccurate Supervision. [PDF, official version, code, bibtex] Zhen-Yu Zhang, Peng Zhao, Yuan Jiang, and Zhi-Hua Zhou. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021, in press.
Improving Deep Forest by Screening. [PDF, official version, bibtex] Ming Pang, Kai Ming Ting, Peng Zhao, and Zhi-Hua Zhou. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021, in press.
Distribution-Free One-Pass Learning. [PDF, official version, code, bibtex] Peng Zhao, Xinqiang Wang, Siyu Xie, Lei Guo, and Zhi-Hua Zhou. IEEE Transaction on Data Engineering (TKDE), 33(3): 951-963, 2021.
Handling Concept Drift via Model Reuse. [PDF, official version, code, bibtex] Peng Zhao, Le-Wen Cai, and Zhi-Hua Zhou. Machine Learning, 109(3): 533-568, 2020.
AAAI-19 Workflow Chair.
Reviewer for Conferences: AAAI (2019, 2020, 2021); AISTATS (2019, 2020, 2021, 2022); ICML (2019, 2020, 2021, 2022); UAI (2019, 2020, 2021, 2022); NeurIPS (2019, 2020, 2021); ICLR (2021,2022); IJCAI (2021, 2022).
Reviewer for Journals: IEEE TKDE, Machine Learning, ACM TKDD, ACM TIST.