I am a Ph.D. student at Institute for Interdisciplinary Information Sciences Tsinghua University advised by Prof. Chongjie Zhang.
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. The Top Ten Most Significant Advances in Chinese Bioinfomatics in 2018 by Genomics Proteomics and Bioinformatics (GPB): Reconstructing spatial organizations of chromosomes through manifold learning [Zhu et al. 2018].
( 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. Zichuan Lin, Li Zhao, Jiang Bian, Guangxiang Zhu, Tao Qin, Guangwen Yang. Gamma Estimation for Variance Reduction in Deep Reinforcement Learning. Under Review. 2019.
3. Guangxiang Zhu*, Jianhao Wang*, Zhizhou Ren*, Zichuan Lin, Chongjie Zhang. Ob
4. 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. [Impact Factor: 12.343]
5. Siyuan Li, Fangda Gu, Guangxiang Zhu, Chongjie Zhang. Context-Aware Policy Reuse. International Conference on Autonomous Agents and MultiAgent Systems (AAMAS). 2019.
6. Guangxiang Zhu, Zhiao Huang, Chongjie Zhang. Ob
7. 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) 46(8) e50-e50. 2018. [Impact Factor: 11.561]
8. Jianbo Guo*, Guangxiang Zhu*, Jian Li. Generative Adversarial Mapping Networks. arXiv preprint arXiv:1709.09820. 2017.
* indicates equal contribution.
Experience and Services
1. AAAI 2020 PC Member
2. Class President, Party Branch Vice Secretary
3. Teaching Assistant for Artificial Intelligence, Deep Reinforcement Learning, and Computational Biology