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The Nystr\"om method has long been popular for scaling up kernel methods.
Über die praktische Auflösung von Integralgleichungen mit Anwendungen auf Randwertaufgaben
E. J. Nyström · 1930
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The coincidence approach to stochastic point processes
O. Macchi · 1975
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Some inequalities for reversible Markov chains
D. J. Aldous · 1982
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Path coupling: A technique for proving rapid mixing in Markov chains
R. Bubley and M. Dyer · 1997
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A more rapidly mixing Markov chain for graph colorings
M. Dyer and C. Greenhill · 1998
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Sparse greedy matrix approximation for machine learning
A. J. Smola and B. Schölkopf · 2000
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Using the Nyström method to speed up kernel machines
C. Williams and M. Seeger · 2001
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Efficient SVM training using low-rank kernel representations
S. Fine and K. Scheinberg · 2002
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Kernel independent component analysis
F. R. Bach and M. I. Jordan · 2003
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Spectral grouping using the Nyström method
C. Fowlkes, S. Belongie, F. Chung, and J. Malik · 2004
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Predictive low-rank decomposition for kernel methods
F. R. Bach and M. I. Jordan · 2005
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On the Nyström method for approximating a Gram matrix for improved kernel-based learning
P. Drineas and M. W. Mahoney · 2005
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Matrix approximation and projective clustering via volume sampling
A. Deshpande, L. Rademacher, S. Vempala, and G. Wang · 2006
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Fast Monte Carlo algorithms for matrices II: Computing a low-rank approximation to a matrix
P. Drineas, R. Kannan, and M. W. Mahoney · 2006
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Determinantal processes and independence
J. B. Hough, M. Krishnapur, Y. Peres, and B. Virág · 2006
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Large-scale manifold learning
A. Talwalkar, S. Kumar, and H. Rowley · 2008
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Improved Nyström low-rank approximation and error analysis
K. Zhang, I. W. Tsang, and J. T. Kwok · 2008
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Negative dependence and the geometry of polynomials
J. Borcea, P. Brändén, and T. Liggett · 2009
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Ensemble Nyström method
S. Kumar, M. Mohri, and A. Talwalkar · 2009
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Fast kernel-based independent component analysis
H. Shen, S. Jegelka, and A. Gretton · 2009
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Sharp analysis of low-rank kernel matrix approximations
F. R. Bach · 2013
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Revisiting the Nyström method for improved large-scale machine learning
A. Gittens and M. W. Mahoney · 2013
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Fast determinantal point process sampling with application to clustering
B. Kang · 2013
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Large-scale SVD and manifold learning
A. Talwalkar, S. Kumar, M. Mohri, and H. Rowley · 2013
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Symmetry and approximability of submodular maximization problems
J. Vondrák · 2013
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Concentration of Lipschitz functionals of determinantal and other strong Rayleigh measures
R. Pemantle and Y. Peres · 2014
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On the impact of kernel approximation on learning accuracy
C. Cortes, M. Mohri, and A. Talwalkar · 2010
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k-DPPs: Fixed-size determinantal point processes
A. Kulesza and B. Taskar · 2011
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Matrix computations
G. H. Golub and C. F. Van Loan · 2012
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Optimal column-based low-rank matrix reconstruction
V. Guruswami and A. K. Sinop · 2012
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Determinantal point processes for machine learning
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Sampling methods for the nyström method
S. Kumar, M. Mohri, and A. Talwalkar · 2012
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Using the matrix ridge approximation to speedup determinantal point processes sampling algorithms
S. Wang, C. Zhang, H. Qian, and Z. Zhang · 2014
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Fast randomized kernel methods with statistical guarantees
A. E. Alaoui and M. W. Mahoney · 2015
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Sampling from probabilistic submodular models
A. Gotovos, H. Hassani, and A. Krause · 2015
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Fast mixing for discrete point processes
P. Rebeschini and A. Karbasi · 2015
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Less is more: Nyström computational regularization
A. Rudi, R. Camoriano, and L. Rosasco · 2015
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A review of Nyström methods for large-scale machine learning
S. Sun, J. Zhao, and J. Zhu · 2015
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Monte Carlo Markov chain algorithms for sampling strongly Rayleigh distributions and determinantal point processes
N. Anari, S. O. Gharan, and A. Rezaei · 2016
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