I’m a Research Assitant Professor in the Department of Computer Science and Engineering, Shanghai Jiao Tong University (SJTU). I obtained a PhD in Compputer Science from Nanyang Technological University (NTU) in 2020. I’m currently working on developing efficient reinforcement learning algorithms to facilitate human-computer interaction. Before coming to Singapore, I got my Bachelor degree from Shanghai Jiao Tong University, in 2015. Here is my CV.
I am particularly interested in developing statistically and computationally efficient machine learning algorithms which are applicable for real-world systems. To improve the sample efficiency in reinforcement learning, one idea I’ve explored is to bootstrap policy gradient with better/worse actions. This leads to fast and unbiased convergence in challenging environments with large action space and short horizon (e.g. intelligent tutoring system) (AAMAS 2019). To reduce the computational cost in cluster analysis, I proposed to exploit curvature information of the evaluation graph. This results in a simple yet powerful method for estimating the number of clusters in a dataset (Information Sciences 2017). I’m also interested in applying machine learning to real-world problems. On this note, I’ve employed an interdisciplinary research method, to design and implement practical gaming systems (a multiplayer game, an online game) and conduct online and offline user studies (Computers in Human Behavior 2018).
Research Interests: Reinforcement Learning; Machine Learning; Human-Computer Interaction.
Email: zhangyaqian [at] sjtu.edu.cn
Yaqian Zhang, Wooi-Boon Goh, “Reinforcement learning-based adaptive task difficulty personalization” User Modeling and User-Adapted Interaction (Under review).
Yaqian Zhang, Wooi-Boon Goh, “Bootstrapped Policy Gradient for Difficulty Adaptation in Intelligent Tutoring Systems” Proceedings of the 18th International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS’19). (paper) (code) (Project Website)
Enmei Tu, Yaqian Zhang, Lin Zhu, Jie Yang, Nikola Kasabov, “A graph-based semi-supervised k nearest-neighbor method for nonlinear manifold distributed data classification”. Information Sciences 367-368 (2016): 673-688.
Enmei Tu, Jie Yang, Nikola Kasabov, Yaqian Zhang, “Posterior Distribution Learning (PDL): A novel supervised learning framework using unlabeled samples to improve classification performance”. Neurocomputing 157(2015): 173-186.
|Reinforcement Learning||Cluster Analysis|
|Enhance sample efficiency with bootstrapped policy gradient with better/worse actions [more]||Curvatue-based method for cluster number determination [more]|
|Difficulty Adaptation||Cooperative Play|
|Robust Dynamic Difficulty Adaptation in intelligent tutoring systems [more]||The influence of peer accountability on attention [more]|