Dr. Zhanxing Zhu (朱占星)
@ Peking University (北京大学)
Welcome to my homepage!
I am now Assistant Professor at School of Mathematical Sciences, and Center for Data Science (大数据科学研究中心), Peking University (北京大学). I am closely affiliated with Deep Learning Lab of Peking University (北京大学深度学习实验室). Previously, I obtained my PhD in machine learning from School of Informatics of University of Edinburgh, UK.
My research interests cover methodology/theory of machine learning and artificial intelligence and their applications in various areas.
- Methodology/theory: deep learning, reinforcement learning, scalable optimization and Bayesian inference methods
- Applications: prediction problems in traffic, data-driven medical imaging, generative models and reinforcement learning for computer graphics and network security
Contact: Room 219, Jingyuan 6th Courtyard, Peking University, Beijing, 100871, China (北京大学静园6号院219)
Email: zhanxing.zhu AT pku.edu.cn
I am always looking for highly self-motivated students/researchers with strong math and/or coding background (preliminary knowledge) to work with me.
- Ph.D students: Bing Yu ( co-supervised with Prof. Weinan E), Yizheng Hu
- MPhil students: Ju Xu, Ke Sun (co-supervised with Prof. Zhouchen Lin), Yuanjin Zhu, Hantao Guo, Junzhao Zhang, Jin Ma.
- Undergraduate: Tianyuan Zhang, Dinghuai Zhang, Kunang Du
- Area Chair/Senior PC for AISTATS 2017, AAAI 2019
- Reviewer for JMLR, TPAMI, NeurIPS, ICML, CVPR, ICCV, AISTATS, AAAI, IJCAI, ACML
Publications/Preprints [Sorted by Research Topics]
* indicates equal contribution. (Check our Github page for reproducibility.)
- Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan and Zhanxing Zhu. The Multiplicative Noise in Stochastic Gradient Descent: Data-Dependent Regularization, Continuous and Discrete Approximation. arXiv pre-print.
- Bing Yu*, Junzhao Zhang* and Zhanxing Zhu. "On the Learning Dynamics of Two-layer Nonlinear Convolutional Neural Networks". arXiv pre-print.
- Lei Wu, Zhanxing Zhu, and Cheng Tai. Understanding and Enhancing the Transferability of Adversarial Examples. arXiv pre-print.
- Guixin Ye, Zhanyong Tang, Dingyi Fang, Zhanxing Zhu, Yansong Feng, Pengfei Xu, Xiaojiang Chen, Jungong Han and. Zheng Wang. Using Generative Adversarial Networks to Break and Protect Text Captchas. ACM Transactions on Privacy and Security, 2019. [TOPS]. (Accepted, to appear.)
- Ke Sun, Zhouchen Lin and Zhanxing Zhu. Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes. The 34th AAAI Conference on Artificial Intelligence. [AAAI 2020]
- Quanming Yao*, Ju Xu*, Wei-wei Tu and Zhanxing Zhu. Efficient Neural Architecture Search via Proximal Iterations. The 34th AAAI Conference on Artificial Intelligence. [AAAI 2020]
- Hantao Guo, Rui Yan, Yansong Feng and Zhanxing Zhu. Simplifying Graph Attention Networks with Source-Target Separation. The 24th European Conference on Artificial Intelligence [ECAI 2020]
- Dinghuai Zhang*, Tianyuan Zhang*, Yiping Lu*, Zhanxing Zhu and Bin Dong. You Only Propagate Once: Accelerating Adversarial Training Using Maximal Principle. 33rd Annual Conference on Neural Information Processing Systems [NeurIPS 2019] . [Code]
- Zhanxing Zhu*, Jingfeng Wu*, Bing Yu, Lei Wu and Jinwen Ma. The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Minima and Regularization Effects. 36th International Conference on Machine Learning [ICML 2019]
- Tianyuan Zhang, Zhanxing Zhu. Interpreting Adversarial Trained Convolutional Neural Networks. 36th International Conference on Machine Learning. [ICML 2019] [Code]
- Bing Yu*, Jingfeng Wu*, Jinwen Ma and Zhanxing Zhu. Tangent-Normal Adversarial Regularization for Semi-supervised Learning. The 30th IEEE Conference on Computer Vision and Pattern Recognition. [CVPR 2019] (Oral presentation) [Code]
- He Wang , Edmond S. L. Ho , Hubert P. H. Shum , and Zhanxing Zhu. Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling. IEEE Transactions on Visualization and Computer Graphics, 2019 [TVCG]
- Ruosi Wan, Mingjun Zhong, Haoyi Xiong and Zhanxing Zhu. Neural Control Variates for Monte Carlo Variance Reduction. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. [ECML/PKDD 2019] [Code]
- Kafeng Wang, Haoyi Xiong, Jiang Bian, Zhanxing Zhu, Chengzhong Xu, Zhishan Guo, Jun Huan. SpHMC: Spectral Hamiltonian Monte Carlo. 33rd AAAI Conference on Artificial Intelligence. [AAAI 2019]
- Ruosi Wan, Haoyi Xiong, Xingjian Li, Zhanxing Zhu, and Jun Huan. Towards Making Deep Transfer Learning Never Hurt. The International Conference on Data Mining. [ICDM 2019] (Regular paper) (To appear)
- Xiantong Zou, Xianghai Zhou, Zhanxing Zhu and Linong Ji. Novel subgroups of patients with adult-onset diabetes in Chinese and US populations. Lancet (Diabetes and Endocrinology) 2019.
- Ke Sun, Zhouchen Lin, and Zhanxing Zhu. Virtual Adversarial Training on Graph Convolutional Networks in Node Classification. Chinese Conference on Pattern Recognition and Computer Vision [PRCV 2019] (Oral presentation) (To appear)
- Sen Hu, Lei Zou and Zhanxing Zhu. How Question Generation Can Help Question Answering over Knowledge Base? International Conference on Natural Language Processing and Chinese Computing [NLPCC 2019]
- Ju Xu, Zhanxing Zhu. Reinforced Continual Learning. 32nd Annual Conference on Neural Information Processing Systems [NeurIPS 2018] [Code]
- Nanyang Ye, Zhanxing Zhu. Bayesian Adversarial Learning. 32nd Annual Conference on Neural Information Processing Systems. [NeurIPS 2018] [Code]
- Rui Luo, Yaodong Yang, Jianhong Wang, Zhanxing Zhu, Jun Wang. Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning. 32nd Annual Conference on Neural Information Processing Systems [NeurIPS 2018] [Code].
- Guixin Ye, Zhanyong Tang, Dingyi Fang, Zhanxing Zhu, Yansong Feng, Pengfei Xu, Xiaojiang Chen, Zheng Wang. Yet Another Text Captcha Solver: A Generative Adversarial Network Based Approach. 25th ACM Conference on Computer and Communications Security . [CCS 2018] (Best Paper Award Finalists) [Code]
- Bing Yu*, Haoteng Yin*, Zhanxing Zhu. Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting. 27th International Joint Conference on Artificial Intelligence . [IJCAI 2018] [Code]
- Nanyang Ye, Zhanxing Zhu. Stochastic Fractional Hamiltonian Monte Carlo. 27th International Joint Conference on Artificial Intelligence. [IJCAI 2018]
- Huizhuo Yuan, Jinzhu Jia and Zhanxing Zhu. SIPID: A Deep Learning Framework for Sinogram Interpolation and Image Denoising in Low-Dose CT Reconstruction. In 2018 IEEE International Symposium on Biomedical Imaging [ISBI 2018] (Oral presentation)[Code]
- Nanyang Ye, Zhanxing Zhu, Rafal K. Mantiuk. Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks. 31st Annual Conference on Neural Information Processing Systems. [NIPS 2017]
- Lei Wu, Zhanxing Zhu and Weinan E. Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes. 34th International Conference on Machine Learning. [ICML 2017 W.]
- Bingfeng Luo, Yansong Feng, Zheng Wang, Zhanxing Zhu, Songfang Huang, Rui Yan and Dongyan Zhao. Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix. 55th Annual Meeting of the Association for Computational Linguistics. [ACL 2017]
- Zhanxing Zhu and Amos Storkey. Stochastic Parallel Block Coordinate Descent for Large-scale Saddle Point Problems. Thirtieth AAAI Conference on Artificial Intelligence [AAAI 2016]
- Zhanxing Zhu*, Xiaocheng Shang*, Benedict Leimkuhler and Amos Storkey. Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling. 29th Annual Conference on Neural Information Processing Systems [NIPS 2015 ]
- Zhanxing Zhu and Amos Storkey. Adaptive Stochastic Primal-Dual Coordinate Descent for Separable Saddle Point Problems. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases [ECML/PKDD 2015]
- Amos Storkey, Zhanxing Zhu and Jinli Hu. Aggregation Under Bias: Renyi Divergence Aggregation and its Implementation via Machine Learning Markets. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. [ECML/PKDD 2015]
- Amos Storkey, Zhanxing Zhu, Jinli Hu. A Continuum from Mixtures to Products: Aggregation under Bias. 31st International Conference on Machine Learning (ICML 2014 Workshop on Divergence Methods for Probabilistic Inference).
- Francesco Corona, Zhanxing Zhu, Amauri Holanda de Souza Júnior, Michela Mulas, Emanuela Muru, Lorenzo Sassu, Guilherme Barreto, and Roberto Baratti. Supervised Distance Preserving Projections: Applications in the quantitative analysis of diesel fuels and light cycle oils from NIR spectra. Journal of Process Control (2014)
- Zhanxing Zhu, Zhirong Yang and Erkki Oja. Multiplicative Updates for Learning with Stochastic Matrices. In the 18th Conference Scandinavian Conferences on Image Analysis (SCIA 2013) (Oral presentation).
- Zhanxing Zhu, Timo Simila and Francesco Corona. Supervised Distance Preserving Projection. Neural Processing Letters 38(3): 445-463 (2013)
- Zhanxing Zhu, Francesco Corona, Amaury Lendasse, Roberto Baratti and Jose A. Romagnoli. Local linear models for soft-sensor design with application to an industrial deethanizer. 18th World Congress of the International Federation of Automatic Control (IFAC) , Milan, Italy, 2011.
- Zhirong Yang, Zhanxing Zhu and Erkki Oja. Automatic Rank Determination in Projective Nonnegative Matrix Factorization. 9th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2010).
" Only in silence the word, only in dark the light, only in dying life: bright the hawk's flight on the empty sky. "
— Ursula K. Le Guin (A Wizard of Earthsea)
Recently, I wrote a book in Chinese with several colleagues on the introduction to data science "数据科学导引". Welcome for all kinds of feedback!
- Ruosi Wan (now research scientist @ Face++)
- Haoteng Yin (now Ph.D student @ Purdue University)
- Jingfeng Wu (now Ph.D student @ John Hopkins University)
- Zizhuo Zhang
- Mengzhang Li (Software Engineer@Canon Medical Systems (China))