I received my B.Sc. degree and M.Sc. degree from Nanjing University, China in 2010 and 2013 respectively. Since Sep. 2013, I become a Ph.D. student in the LAMDA Group, Nanjing University, under the supervision of Prof. Zhi-Hua Zhou.
In Jan. 2015, I aborted the Ph.D. procedure and joined the search algorithm team at Alibaba. Now I am a senior algorithm expert of the AI team within the search department of Alibaba.
My research interests include machine learning, data mining.
- Qing Da, An-Xiang Zeng, Reinforcement Learning Beyond Games: To Make a difference in Alibaba. Electronic Industry Press, 2018. Link
- Guangda Huzhang, Zhen-Jia Pang, Yongqing Gao, Wen-Ji Zhou, Qing Da, An-Xiang Zeng, Yang Yu. Validation Set Evaluation can be Wrong: An Evaluator-Generator Approach for Maximizing Online Performance of Ranking in E-commerce, CORR abs/2003.11941, 2020.
- Anxiang Zeng, Han Yu, Qing Da, Yusen Zhan, Chun-yanMiao, Accelerating E-Commerce Search Engine Ranking by Contextual Factor Selection. In: Proceedings of the 34rd AAAI Conference on Artificial Intelligence (AAAI-20 / IAAI-20), New York, USA, 2020.
- Pengcheng Li, Runze Li, Qing Da, An-Xiang Zeng, Lijun Zhang. Improving Multi-Scenario Learning to Rank in E-commerce by Exploiting Task Relationships in the Label Space. In: Proceedings of the 29th International Conference on Information and Knowledge Management (CIKM’20), Virtual Event, Ireland, 2020.
- Jing-Cheng Shi, Yang Yu, Qing Da, Shi-Yong Chen, An-Xiang Zeng. Virtual-Taobao: Virtualizing real-world online retail environment for reinforcement learning.In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI’19), Honolulu, HI, 2019. (PDF).
- Feiyang Pan, Qingpeng Cai , An-Xiang Zeng , Chun-Xiang Pan, Qing Da, Hualin He, Qing He, Pingzhong Tang. Policy Optimization with Model-based Explorations. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI’19), Honolulu, HI, 2019. (PDF).
- Yusen Zhan, Qing Da, Fei Xiao, An-xiang Zeng, Yang Yu, Accelerating E-Commerce Search Engine Ranking by Contextual Factor Selection, CORR abs/1803.00693
- Hua-Lin Hei, Chun-Xiang Pan, Qing Da, An-Xiang Zeng. Speeding up the Metabolism in E-commerce by Reinforcement Mechanism Design. In: “Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD’18)“, Dublin, Ireland, 2018. PDF
- Shi-Yong Chen, Yang Yu, Qing Da, Jun Tan, Hai-Kuan Huang and Hai-Hong Tang. Stablizing reinforcement learning in dynamic environment with application to online recommendation. In: Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’18) (Research Track), London, UK, 2018. PDF
- Yujing Hu, Qing Da, Anxiang Zeng, Yang Yu, Yinghui Xu, Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application. In: Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD’18) (Applied Track), London, UK, 2018. PDF
- Yang Yu, Shi-Yong Chen, Qing Da, Zhi-Hua Zhou. Reusable reinforcement learning via shallow trails. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(6): 2204-2215. PDF
- Yang Yu, Peng-Fei Hou, Qing Da, and Yu Qian. Boosting nonparametric policies. In: Proceedings of the 2016 International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS’16), Singapore, 2016, pp.477-484. PDF
- Yang Yu and Qing Da, PolicyBoost: Functional policy gradient with ranking-based reward objective. In: Proceedings of AAAI Workshop on AI and Robotics (AIRob’14), Quebec City, Canada, 2014. PDF
- Qing Da, Yang Yu, and Zhi-Hua Zhou. Learning with Augmented Class by Exploiting Unlabeled Data. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI’14), Québec city, Canada, 2014. PDF
- Qing Da, Yang Yu, and Zhi-Hua Zhou. Napping for Functional Representation of Policy. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS’14), Paris, France, 2014. PDF
- Qing Da, Yang Yu, and Zhi-Hua Zhou. Self-Practice Imitation Learning from Weak Policy. In: Proceedings of the 2nd IAPR International Workshop on Partially Supervised Learning (PSL’13), Nanjing, China, 2013, pp.9-20. PDF
- Data Mining (for M.Sc. students), Fall, 2014
- Data Mining (for M.Sc. students), Fall, 2013
- Digital Image Processing (for undergraduate student), Spring, 2013
Awards & Honors
- OpenAI Retro Contest (Transfer Learning in Reinforcement learning), Champion, 2018
- National Graduate Scholarship, 2012
- First prize of Internet contest for Cloud & Mobile computing (for image search track), 2012
- Grand Prize Winner of the PAKDD 2012 Data Mining Competition (Open Category) (with Nan Li, Chao Qian, Shao-Yuan Li, Yue Zhu, and Zhi-Hua Zhou), 2012
- Outstanding project (six in total over the nation) of China Innovation Program for Students (sponsored by Sun), 2010 (project page)
- Computer World Scholarship, 2009
- First prize in China Undergraduate Mathematical Contest in Modeling (CUMCM), 2008