Xiangxiang Xu


Ph.D.

Research Assistant, Tsinghua-Berkeley Shenzhen Institute

CV, Google Scholar

Email: xiangxiangxu AT ieee.org


My research focuses on information theory and its applications in data analytics.

Selective Publications

  1. Xiangxiang Xu and Shao-Lun Huang. An Information Theoretic Framework for Distributed Learning Algorithms. In IEEE International Symposium on Information Theory (ISIT), 2021. [Paper] [Slides]
  2. Shao-Lun Huang, Xiangxiang Xu, and Lizhong Zheng. An information-theoretic approach to unsupervised feature selection for high-dimensional data. In IEEE Journal on Selected Areas in Information Theory (2020).
  3. Xiangxiang Xu and Shao-Lun Huang. Maximal Correlation Regression. In: IEEE Access 8 (2020), pp. 26591-26601. [Paper]
  4. Shao-Lun Huang, Xiangxiang Xu, Lizhong Zheng, and Gregory W. Wornell. An Information Theoretic Interpretation to Deep Neural Networks. In IEEE International Symposium on Information Theory (ISIT), 2019. [Paper] [arXiv]
  5. Lichen Wang, Jiaxiang Wu, Shao-Lun Huang, Lizhong Zheng, Xiangxiang Xu, Lin Zhang, and Junzhou Huang. An Efficient Approach to Informative Feature Extraction from Multimodal Data. In AAAI 2019. [Paper] [arXiv]
  6. Xiangxiang Xu, Shao-Lun Huang, Lizhong Zheng, and Lin Zhang. The Geometric Structure of Generalized Softmax Learning. In 2018 IEEE Information Theory Workshop (ITW) (IEEE ITW 2018), Guangzhou, P.R. China, November 2018. [Paper] [Slides]