Journal Papers

  • Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds.
    Shusen Wang, Alex Gittens, and Michael W. Mahoney.
    Accepted by Journal of Machine Learning Research (JMLR), conditioned on minor revisions.
    arXiv:1706.02803.

  • Sketched Ridge Regression: Optimization Perspective, Statistical Perspective, and Model Averaging.
    Shusen Wang, Alex Gittens, and Michael W. Mahoney.
    Journal of Machine Learning Research, 18:1-50, 2018. (JMLR 2018).
    A short version has appeared in ICML 2017. (There are errors in the ICML version; please refer to the journal version for the correct results.)
    [pdf]

  • 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).
    Short versions have appeared in AISTATS 2014 and KDD 2014.
    [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).
    A short version has appeared in NIPS 2012.
    [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]

Preprints

  • GIANT: Globally Improved Approximate Newton Method for Distributed Optimization.
    Shusen Wang, Farbod Roosta-Khorasani, Peng Xu, and Michael W. Mahoney.
    arXiv:1709.03528, 2017.
    [Spark Code] [Large-Scale Experiments]

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

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

Conference Papers

  • Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap.
    Miles E. Lopes, Shusen Wang, Michael W. Mahoney.
    In International Conference on Machine Learning, 2018. (ICML 2018).
    [available on arXiv]

  • Alchemist: Accelerating Large-Scale Data Analysis by Offloading to High-Performance Computing Libraries.
    Alex Gittens, Kai Rothauge, Shusen Wang, Michael W. Mahoney, Lisa Gerhardt, Prabhat, Jey Kottalam, Michael Ringenburg, and Kristyn Maschhoff.
    In ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2018. (KDD 2018).

  • 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]