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Michael W. Mahoney: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Petros Drineas, Michael W. Mahoney, S. Muthukrishnan
    Subspace Sampling and Relative-Error Matrix Approximation: Column-Based Methods. [Citation Graph (0, 0)][DBLP]
    APPROX-RANDOM, 2006, pp:316-326 [Conf]
  2. Petros Drineas, Michael W. Mahoney
    Approximating a Gram Matrix for Improved Kernel-Based Learning. [Citation Graph (0, 0)][DBLP]
    COLT, 2005, pp:323-337 [Conf]
  3. Petros Drineas, Michael W. Mahoney, S. Muthukrishnan
    Subspace Sampling and Relative-Error Matrix Approximation: Column-Row-Based Methods. [Citation Graph (0, 0)][DBLP]
    ESA, 2006, pp:304-314 [Conf]
  4. Ravi Kannan, Michael W. Mahoney, Ravi Montenegro
    Rapid Mixing of Several Markov Chains for a Hard-Core Model. [Citation Graph (0, 0)][DBLP]
    ISAAC, 2003, pp:663-675 [Conf]
  5. Michael W. Mahoney, Mauro Maggioni, Petros Drineas
    Tensor-CUR decompositions for tensor-based data. [Citation Graph (0, 0)][DBLP]
    KDD, 2006, pp:327-336 [Conf]
  6. Petros Drineas, Michael W. Mahoney, S. Muthukrishnan
    Sampling algorithms for l2 regression and applications. [Citation Graph (0, 0)][DBLP]
    SODA, 2006, pp:1127-1136 [Conf]
  7. Petros Drineas, Ravi Kannan, Michael W. Mahoney
    Sampling Sub-problems of Heterogeneous Max-cut Problems and Approximation Algorithms. [Citation Graph (0, 0)][DBLP]
    STACS, 2005, pp:57-68 [Conf]
  8. Petros Drineas, Michael W. Mahoney
    Randomized Algorithms for Matrices and Massive Data Sets. [Citation Graph (0, 0)][DBLP]
    VLDB, 2006, pp:1269- [Conf]
  9. Petros Drineas, Michael W. Mahoney
    On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2005, v:6, n:, pp:2153-2175 [Journal]
  10. Andreas Frommer, Michael W. Mahoney, Daniel B. Szyld
    07071 Report on Dagstuhl Seminar -- Web Information Retrieval and Linear Algebra Algorithms. [Citation Graph (0, 0)][DBLP]
    Web Information Retrieval and Linear Algebra Algorithms, 2007, pp:- [Conf]
  11. Andreas Frommer, Michael W. Mahoney, Daniel B. Szyld
    07071 Abstracts Collection -- Web Information Retrieval and Linear Algebra Algorithms. [Citation Graph (0, 0)][DBLP]
    Web Information Retrieval and Linear Algebra Algorithms, 2007, pp:- [Conf]
  12. Anirban Dasgupta, Petros Drineas, Boulos Harb, Vanja Josifovski, Michael W. Mahoney
    Feature selection methods for text classification. [Citation Graph (0, 0)][DBLP]
    KDD, 2007, pp:230-239 [Conf]
  13. Anirban Dasgupta, Petros Drineas, Boulos Harb, Ravi Kumar, Michael W. Mahoney
    Sampling Algorithms and Coresets for Lp Regression [Citation Graph (0, 0)][DBLP]
    CoRR, 2007, v:0, n:, pp:- [Journal]
  14. Petros Drineas, Michael W. Mahoney, S. Muthukrishnan
    Relative-Error CUR Matrix Decompositions [Citation Graph (0, 0)][DBLP]
    CoRR, 2007, v:0, n:, pp:- [Journal]
  15. Petros Drineas, Michael W. Mahoney, S. Muthukrishnan, Tamás Sarlós
    Faster Least Squares Approximation [Citation Graph (0, 0)][DBLP]
    CoRR, 2007, v:0, n:, pp:- [Journal]
  16. Petros Drineas, Ravi Kannan, Michael W. Mahoney
    Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix. [Citation Graph (0, 0)][DBLP]
    SIAM J. Comput., 2006, v:36, n:1, pp:158-183 [Journal]
  17. Petros Drineas, Ravi Kannan, Michael W. Mahoney
    Fast Monte Carlo Algorithms for Matrices III: Computing a Compressed Approximate Matrix Decomposition. [Citation Graph (0, 0)][DBLP]
    SIAM J. Comput., 2006, v:36, n:1, pp:184-206 [Journal]
  18. Petros Drineas, Ravi Kannan, Michael W. Mahoney
    Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication. [Citation Graph (0, 0)][DBLP]
    SIAM J. Comput., 2006, v:36, n:1, pp:132-157 [Journal]

  19. Unsupervised feature selection for principal components analysis. [Citation Graph (, )][DBLP]


  20. Sampling algorithms and coresets for ℓp regression. [Citation Graph (, )][DBLP]


  21. An improved approximation algorithm for the column subset selection problem. [Citation Graph (, )][DBLP]


  22. Empirical Evaluation of Graph Partitioning Using Spectral Embeddings and Flow. [Citation Graph (, )][DBLP]


  23. Statistical properties of community structure in large social and information networks. [Citation Graph (, )][DBLP]


  24. Empirical comparison of algorithms for network community detection. [Citation Graph (, )][DBLP]


  25. Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters [Citation Graph (, )][DBLP]


  26. Algorithmic and Statistical Challenges in Modern Large-Scale Data Analysis are the Focus of MMDS 2008 [Citation Graph (, )][DBLP]


  27. An Improved Approximation Algorithm for the Column Subset Selection Problem [Citation Graph (, )][DBLP]


  28. Learning with Spectral Kernels and Heavy-Tailed Data [Citation Graph (, )][DBLP]


  29. A Spectral Algorithm for Improving Graph Partitions [Citation Graph (, )][DBLP]


  30. Empirical Comparison of Algorithms for Network Community Detection [Citation Graph (, )][DBLP]


  31. Effective Resistances, Statistical Leverage, and Applications to Linear Equation Solving [Citation Graph (, )][DBLP]


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