Jingwei Zhuo |
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Biography
I am now at Alibaba, working on some exciting projects about extremely large scale retrieval in targeted advertising! Before that, I received my Ph.D. degree in 2019 from Department of Computer Science and Technology of Tsinghua University, co-advised by Prof. Jun Zhu and Prof. Bo Zhang. I received my M.Sc. degree in 2015 from Computer Science Department of Carnegie Mellon University and my B.Sc. degree in 2014 from Department of Electronics Engineering of Tsinghua University.
Publications
- Jingwei Zhuo, Ziru Xu, Wei Dai, Han Zhu, Han Li, Jian Xu, Kun Gai. Learning Optimal Tree Models Under Beam Search, in the International Conference on Machine Learning, Online, 2020 (ICML 2020). [supplement materials]
- Chang Liu, Jingwei Zhuo, and Jun Zhu. Understanding MCMC Dynamics as Flows on the Wasserstein Space, in International Conference on Machine Learning, Long Beach, USA, 2019 (ICML 2019).
- Chang Liu, Jingwei Zhuo, Pengyu Cheng, Ruiyi Zhang, Jun Zhu, and Lawrence Carin. Understanding and Accelerating Particle-Based Variational Inference, in International Conference on Machine Learning, Long Beach, USA, 2019 (ICML 2019).
- Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen and Bo Zhang. Message Passing Stein Variational Gradient Descent, in International Conference on Machine Learning, Stockholm, Sweden, 2018 (ICML 2018). [supplement materials] [poster]
- Yichi Zhou, Jun Zhu, and Jingwei Zhuo. Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors, in International Conference on Machine Learning, Stockholm, Sweden, 2018 (ICML 2018).
- Wenbo Hu, Jun Zhu, Hang Su, Jingwei Zhuo, and Bo Zhang. Semi-supervised Max-margin Topic Models with Manifold Posterior Regularization, in International Joint Conference on Artificial Intelligence, Melbourne, Australia, 2017 (IJCAI 2017).
- Jingwei Zhuo, Yong Cao, Jun Zhu, Bo Zhang, and Zaiqing Nie. Segment-Level Sequence Modeling using Gated Recursive Semi-Markov Conditional Random Fields, in the Annual Meeting of the Association for Computational Linguistics, Berlin, Germany, 2016 (ACL 2016). [supplement materials]
- Jingwei Zhuo, Jun Zhu, and Bo Zhang. Adaptive Dropout Rates for Learning with Corrupted Features, in International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina, 2015 (IJCAI 2015).
- Justin P. Haldar, and Jingwei Zhuo. P-LORAKS: Low-rank modeling of local k-space neighborhoods with parallel imaging data, Magnetic Resonance in Medicine, 2016 (MRM).
- Jingwei Zhuo, and Justin P. Haldar. P-LORAKS: Low-rank modeling of local k-space neighborhoods with parallel imaging data, in International Society for Magnetic Resonance in Medicine, Milan, Italy, 2014 (ISMRM 2014). [Recipient of a Magna Cum Laude ISMRM Merit Award]
arXiv Preprints
- Jianqiao Wangni, Jingwei Zhuo, Jun Zhu. Learning Random Fourier Features by Hybrid Constrained Optimization, arXiv:1712.02527, 2017.
Services
Conference Reviewer- ICML 2018/2019/2020
- NeurIPS 2018/2019
- ICLR 2019/2020
- AISTATS 2019/2020
- AAAI 2017/2018/2019/2020
- ACML 2018/2019/2020
- ICPR 2016/2018/2020
- ICCSIP 2016
- ACM Transactions on Knowledge Discovery from Data (TKDD), in 2018