I am a Ph.D. candidate in Computer Science at Institute for Interdisciplinary Information Sciences (IIIS) Tsinghua University advised by Prof. Chongjie Zhang. My homepage is https://guangxiangzhu.github.io/.
My research interests include Reinforcement Learning and Deep Learning. My main goal is to improve the sample efficiency of reinforcement learning via efficient representation learning, episodic control, and model-based approaches.
1. Top 10 Breakthroughs of 2018 in Chinese Bioinfomatics: Reconstructing spatial organizations of chromosomes through manifold learning [Zhu et al. 2018], awarded by Genomics Proteomics and Bioinformatics (GPB).
See Tsinghua News.
2. National Scholarship (top 1%) at Tsinghua University, 2019
3. Champion of Shandong Division in the Fourth China "Internet Plus" Student Innovation and Entrepreneurship Competition, 2018
4. Computing Accreditation for Professionals (Ranking: 5/372) certificated by China Computer Federation (CCF), 2015
5. Tsinghua-Baidu Future Star Third-Class Scholarship, 2018
1. Guangxiang Zhu*, Zichuan Lin*, Guangwen Yang, Chongjie Zhang. Episodic Reinforcement Learning with Associative Memory. Eighth International Conference on Learning Representations (ICLR 2020).
2. Guangxiang Zhu*, Jianhao Wang*, Zhizhou Ren*, Zichuan Lin, Chongjie Zhang. Ob
3. Abbas Ahmed, Xuan He, Jing Niu, Bin Zhou, Guangxiang Zhu, Tszshan Ma, Juntao Gao, Michael Zhang, Jianyang Zeng. Integrating Hi-C and FISH data for modeling 3D organizations of chromosomes. Nature Communications. 2019, 10(1): 1-14. [Impact Factor: 12.343]
4. Siyuan Li, Fangda Gu, Guangxiang Zhu, Chongjie Zhang. Context-Aware Policy Reuse. International Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2019).
5. Guangxiang Zhu, Zhiao Huang, Chongjie Zhang. Ob
6. Guangxiang Zhu, Wenxuan Deng, Hailin Hu, Rui Ma, Sai Zhang, Jinglin Yang, Jian Peng, Tommy Kaplan, Jianyang Zeng. Reconstructing spatial organizations of chromosomes through manifold learning. Nucleic Acids Research (NAR), 2018, 46(8) e50-e50. [Impact Factor: 11.561]
7. Jianbo Guo*, Guangxiang Zhu*, Jian Li. Generative Adversarial Mapping Networks. arXiv preprint arXiv:1709.09820. 2017.
8. Zhizhou Ren, Guangxiang Zhu, Beining Han, Jianglun Chen and Chongjie Zhang. Towards Understanding Value Function Approximation: Rethinking the Effects of Twin Q-Networks. Under Review, 2020
9. Zichuan Lin, Li Zhao, Jiang Bian, Guangxiang Zhu, Tao Qin, Guangwen Yang. Gamma Estimation for Variance Reduction in Deep Reinforcement Learning. Under Review. 2020.
10. Tongyu Wang, Shangmei Zhao, Guangxiang Zhu, Haitao Zheng. A Machine Learning-Based Early Warning System for Systemic Banking Crises. Under Review, 2020
* indicates equal contribution.
Experience and Services
AAAI 2020 PC Member
IJCAI 2020 PC Member
NeurIPS 2020 PC Member
Deep Reinforcement Learning (Graduate Course, Spring 2018)
Artificial Intelligence (Undergraduate Course, Fall 2017)
Computational Biology (Graduate Course, Fall 2016)
C Programming (Undergraduate Course, Fall 2014)
Class President (2018-present)