Xiangxiang Xu


Postdoctoral Researcher, EECS, MIT

CV, Google Scholar

Email: xuxx AT mit.edu


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

Selective Publications

  1. Xiangxiang Xu and Shao-Lun Huang. On Distributed Hypothesis Testing with Constant-Bit Communication Constraints. In 2021 IEEE Information Theory Workshop (IEEE ITW 2021). [Paper] [Slides]
  2. 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]
  3. Shao-Lun Huang and Xiangxiang Xu. On the Sample Complexity of HGR Maximal Correlation Functions for Large Datasets. In: IEEE Transactions on Information Theory 67.3 (2021), pp. 1951–1980.
  4. 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). [Link]
  5. Xiangxiang Xu and Shao-Lun Huang. Maximal Correlation Regression. In: IEEE Access 8 (2020), pp. 26591-26601. [Paper]
  6. 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]
  7. 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]
  8. 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]