Journal Papers

  • Towards More Efficient SPSD Matrix Approximation and CUR Matrix Decomposition.
    Shusen Wang, Zhihua Zhang, and Tong Zhang.
    Journal of Machine Learning Research, 17(210):1−49, 2016. (JMLR 2016).
    [pdf] [bib]

  • SPSD Matrix Approximation vis Column Selection: Theories, Algorithms, and Extensions.
    Shusen Wang, Luo Luo, and Zhihua Zhang.
    Journal of Machine Learning Research, 17(49):1-49, 2016. (JMLR 2016).
    [pdf] [bib]

  • Improving CUR Matrix Decomposition and the Nystrom Approximation via Adaptive Sampling.
    Shusen Wang and Zhihua Zhang.
    Journal of Machine Learning Research, 14: 2729-2769, 2013. (JMLR 2013).
    [pdf] [bib]

  • EP-GIG Priors and Applications in Bayesian Sparse Learning.
    Zhihua Zhang, Shusen Wang, Dehua Liu, and Michael I. Jordan.
    Journal of Machine Learning Research, 13: 2031-2061, 2012. (JMLR 2012).
    [pdf] [bib]

Conference Papers

  • Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging.
    Shusen Wang, Alex Gittens, and Michael W. Mahoney.
    In International Conference on Machine Learning, 2017. (ICML 2017).
    [pdf] [bib] [long version]

  • Towards Real-Time Geologic Feature Detection from Seismic Measurements using a Randomized Machine-Learning Algorithm.
    Youzuo Lin, Shusen Wang, Jayaraman Thiagarajan, George Guthrie, and David Coblentz.
    In Proceeding of Society of Exploration Geophysics (SEG), 2017.

  • Open Domain Short Text Conceptualization: A Generative + Descriptive Modeling Approach.
    Yangqiu Song, Shusen Wang, and Haixun Wang.
    In International Joint Conference on Artificial Intelligence, 2015. (IJCAI 2015).
    [pdf]

  • Improving the Modified Nystrom Method Using Spectral Shifting.
    Shusen Wang, Chao Zhang, Hui Qian, and Zhihua Zhang.
    In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2014. (KDD 2014).
    [pdf]

  • Efficient Algorithms and Error Analysis for the Modified Nystrom Method.
    Shusen Wang and Zhihua Zhang.
    In International Conference on Artificial Intelligence and Statistics, JMLR W&CP, 2014. (AISTATS 2014).
    [arXiv:1404.0138] [bib] [code] [slides]

  • Making Fisher Discriminant Analysis Scalable.
    Bojun Tu, Zhihua Zhang, Shusen Wang, and Hui Qian.
    In International Conference on Machine Learning . (ICML 2014).

  • Exact Subspace Clustering in Linear Time.
    Shusen Wang, Bojun Tu, Congfu Xu, and Zhihua Zhang.
    In AAAI Conference on Artificial Intelligence. (AAAI 2014).
    [pdf] [bib]

  • Using The Matrix Ridge Approximation to Speedup Determinantal Point Processes Sampling Algorithms.
    Shusen Wang, Chao Zhang, Hui Qian, and Zhihua Zhang.
    In AAAI Conference on Artificial Intelligence. (AAAI 2014).
    [pdf] [bib]

  • Transfer Understanding from Head Queries to Tail Queries.
    Yangqiu Song, Haixun Wang, Weizhu Chen, and Shusen Wang.
    In ACM International Conference on Information and Knowledge Management. (CIKM 2014).
    [pdf]

  • Nonconvex Relaxation Approaches to Robust Matrix Recovery.
    Shusen Wang, Dehua Liu, and Zhihua Zhang.
    In International Joint Conference on Artificial Intelligence. (IJCAI 2013).
    [pdf] [bib] [code] [slides] [poster]

  • A Scalable CUR Matrix Decomposition Algorithm: Lower Time Complexity and Tighter Bound.
    Shusen Wang and Zhihua Zhang.
    In Advances in Neural Information Processing Systems. (NIPS 2012).
    [pdf] [bib] [code]

  • Colorization by Matrix Completion.
    Shusen Wang and Zhihua Zhang.
    In AAAI Conference on Artificial Intelligence. (AAAI 2012).
    [pdf] [bib] [code] [data] [poster]

  • Efficient Subspace Segmentation via Quadratic Programming.
    Shusen Wang, Xiaotong Yuan, Tiansheng Yao, Shuicheng Yan, and Jialie Shen.
    In AAAI Conference on Artificial Intelligence. (AAAI 2011).
    [pdf] [bib] [code]

Preprints

  • A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication.
    Miles E. Lopes, Shusen Wang, Michael W. Mahoney.
    arXiv:1708.01945, 2017.

  • Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds.
    Shusen Wang, Alex Gittens, and Michael W. Mahoney.
    arXiv:1706.02803, 2017.

  • Improved Analyses of the Randomized Power Method and Block Lanczos Method.
    Shusen Wang, Zhihua Zhang, and Tong Zhang.
    arXiv:1508.06429, 2015.