Statistics Faculty

Feng Liang

  • Associate Professor
  • Department of Statistics
  • University of Illinois at Urbana-Champaign
  • 104F Illini Hall
  • 725 S. Wright St.
  • Champaign, IL 61820 USA
  • (217) 333-6017
  • Website
  • Educational Background

  • PhD, Statistics, Yale University, 2002
  • Research Interests

  • Bayesian methods
  • Decision theory
  • Information theory
  • Minimum description length principle
  • Data mining
  • Recent Publications

    Y. Yang, F. Liang, and T. S. Huang (2014) Discriminative Exemplar Clustering. Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2014.

    H. Wang, C. Zhai, F. Liang, A. Dong and Y. Chang (2014) User Modeling in Search Logs via A Nonparametric Bayesian Approach. The 7th ACM Web Search and Data Mining Conference (WSDM'2014).

    J. Ridgway, P. Alquier, N. Chopin, F. Liang. (2014). PAC-Bayesian AUC classification and scoring. In Advances in Neural Information Processing Systems 27 (NIPS 2014).

    Y. Yang, F. Liang, S. Yan, Z. Wang, T. Huang. (2014). On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification. In Advances in Neural Information Processing Systems 27 (NIPS 2014).

    B. Li, F. Liang, J. Hu, and X. He (2012) Reno: Regularized Nonparametric Analysis of Protein Lysate Array Data. Bioinformatics 28(9):1223-1229.

    E. I. George, F. Liang and X. Xu (2012) From Minimax Shrinkage Estimation to Minimax Shrinkage Prediction. Statistical Science 27:82-94.

    Y. Yang, X. Chu, F. Liang, and T. Huang (2012) Pairwise Exemplar Clustering. In Proceedings of the 26th Conference on Artificial Intelligence, Toronto, Canada.

    J. Xu and F. Liang (2010). Bayesian co-segmentation of multiple MR images. Statistics and Its Interface, 3:513-521.

    X. Xu and F. Liang (2010). Asymptotic minimax risk of predictive density estimation for nonparametric regression. Bernoulli, 16(2):543-560.

    Gao J, Liang F, Fan W, Wang C, Sun Y, and Han J. (2010). On community outliers and their efficient detection in information networks. In Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Washington, DC.