Publications

Below is a selected collection of my publications. A full list can be found at my Google Scholar profile.

  1. Xu, Xiangxiang, Lizhong Zheng, and Ishank Agrawal. Neural Feature Learning for Engineering Problems. 2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton).
  2. Xu, Xiangxiang and Lizhong Zheng. Sequential Dependence Decomposition and Feature Learning. 2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton). [Slides] [Demo]
  3. Xu, Xiangxiang and Lizhong Zheng. A Geometric Framework for Neural Feature Learning. arXiv preprint arXiv:2309.10140 (2023).
  4. Xu, Xiangxiang and Lizhong Zheng. Kernel Subspace and Feature Extraction. 2023 IEEE International Symposium on Information Theory (ISIT). IEEE. 2023, pp. 1032–1037. [Slides]
  5. Xu, Xiangxiang and Shao-Lun Huang. On Distributed Learning with Constant Communication Bits. In IEEE Journal on Selected Areas in Information Theory (2022). [Slides] 
  6. Xu, Xiangxiang, Shao-Lun Huang, Lizhong Zheng, and Gregory W. Wornell. 2022. "An Information Theoretic Interpretation to Deep Neural Networks" Entropy 24, no. 1: 135. [Code]
  7. Huang, Shao-Lun, 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). [Appendix]
  8. Xu, Xiangxiang and Shao-Lun Huang. Maximal Correlation Regression. In: IEEE Access 8 (2020), pp. 26591-26601. [Code] 
  9. Xu, Xiangxiang, 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. [Slides] [Code]