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Randomized Numerical Linear Algebra (RandNLA) uses randomness to develop improved algorithms for matrix problems that arise in scientific computing, data science, machine learning, etc.
Über die praktische auflösung von integralgleichungen mit anwendungen auf randwertaufgaben
E. J. Nyström · 1930
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Theory of optimal experiments
Valerii V Fedorov · 1972
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The coincidence approach to stochastic point processes
Odile Macchi · 1975
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Extensions of Lipschitz mappings into a Hilbert space
William B Johnson and Joram Lindenstrauss · 1984
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Distributions on partitions, point processes, and the hypergeometric kernel
A. Borodin and G. Olshanski · 2000
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Using the Nyström method to speed up kernel machines
Christopher K. I. Williams and Matthias Seeger · 2001
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Asymptotic analysis of sampling estimators for randomized numerical linear algebra algorithms
P. Ma, X. Zhang, X. Xing, J. Ma, and M. W. Mahoney · 2002
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Database-friendly random projections: Johnson-Lindenstrauss with binary coins
Dimitris Achlioptas · 2003
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Kernel independent component analysis
Francis R. Bach and Michael I. Jordan · 2003
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Matrix approximation and projective clustering via volume sampling
Amit Deshpande, Luis Rademacher, Santosh Vempala, and Grant Wang · 2006
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Fast Monte Carlo algorithms for matrices I: Approximating matrix multiplication
P. Drineas, R. Kannan, and M. W. Mahoney · 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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Fast Monte Carlo algorithms for matrices III: Computing a compressed approximate matrix decomposition
P. Drineas, R. Kannan, and M. W. Mahoney · 2006
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Sampling algorithms for
Petros Drineas, Michael W Mahoney, and S Muthukrishnan · 2006
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Determinantal processes and independence
J Ben Hough, Manjunath Krishnapur, Yuval Peres, and Bálint Virág · 2006
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Improved approximation algorithms for large matrices via random projections
Tamas Sarlos · 2006
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Relative-error CUR matrix decompositions
Petros Drineas, Michael W. Mahoney, and S. Muthukrishnan · 2008
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The fast Johnson–Lindenstrauss transform and approximate nearest neighbors
Nir Ailon and Bernard Chazelle · 2009
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Spectral methods in machine learning and new strategies for very large datasets
Mohamed-Ali Belabbas and Patrick J. Wolfe · 2009
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Negative dependence and the geometry of polynomials
Julius Borcea, Petter Brändén, and Thomas Liggett · 2009
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An improved approximation algorithm for the column subset selection problem
C. Boutsidis, M. W. Mahoney, and P. Drineas · 2009
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Blendenpik: Supercharging lapack’s least-squares solver
Haim Avron, Petar Maymounkov, and Sivan Toledo · 2010
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Efficient volume sampling for row/column subset selection
Amit Deshpande and Luis Rademacher · 2010
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Faster least squares approximation
P. Drineas, M. W. Mahoney, S. Muthukrishnan, and T. Sarlós · 2010
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Structured determinantal point processes
Alex Kulesza and Ben Taskar · 2010
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Determinantal point processes
A. Borodin · 2011
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Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
N. Halko, P.-G. Martinsson, and J. A. Tropp · 2011
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k-DPPs: Fixed-Size Determinantal Point Processes
Alex Kulesza and Ben Taskar · 2011
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Randomized algorithms for matrices and data
M. W. Mahoney · 2011
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Fast approximation of matrix coherence and statistical leverage
Petros Drineas, Malik Magdon-Ismail, Michael W. Mahoney, and David P. Woodruff · 2012
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Optimal column-based low-rank matrix reconstruction
Venkatesan Guruswami and Ali K. Sinop · 2012
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Unbiased estimates for linear regression via volume sampling
Michał Dereziński and Manfred K. Warmuth · 2017
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Zonotope hit-and-run for efficient sampling from projection DPPs
Guillaume Gautier, Rémi Bardenet, and Michal Valko · 2017
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Elementary symmetric polynomials for optimal experimental design
Zelda E. Mariet and Suvrit Sra · 2017
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Determinantal point processes for mini-batch diversification
Cheng Zhang, Hedvig Kjellström, and Stephan Mandt · 2017
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Reverse iterative volume sampling for linear regression
Michał Dereziński and Manfred K. Warmuth · 2018
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Subsampling for ridge regression via regularized volume sampling
Michał Dereziński and Manfred K. Warmuth · 2018
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Determinantal Point Processes for Machine Learning
Alex Kulesza and Ben Taskar · 2012
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Approximate computation and implicit regularization for very large-scale data analysis
M. W. Mahoney · 2012
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User-friendly tail bounds for sums of random matrices
Joel A. Tropp · 2012
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Nystrom approximation for large-scale determinantal processes
Raja Hafiz Affandi, Alex Kulesza, Emily Fox, and Ben Taskar · 2013
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Faster subset selection for matrices and applications
Haim Avron and Christos Boutsidis · 2013
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OSNAP: Faster numerical linear algebra algorithms via sparser subspace embeddings
J. Nelson and H. L. Nguyen · 2013
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Leveraged volume sampling for linear regression
Michał Dereziński, Manfred K. Warmuth, and Daniel Hsu · 2018
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Proportional volume sampling and approximation algorithms for A-optimal design
Aleksandar Nikolov, Mohit Singh, and Uthaipon Tao Tantipongpipat · 2018
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On fast leverage score sampling and optimal learning
Alessandro Rudi, Daniele Calandriello, Luigi Carratino, and Lorenzo Rosasco · 2018
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Kernel quadrature with DPPs
Ayoub Belhadji, Rémi Bardenet, and Pierre Chainais · 2019
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Reconciling modern machine learning practice and the classical bias-variance trade-off
M. Belkin, D. Hsu, S. Ma, and S. Mandal · 2019
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Rates of convergence for sparse variational Gaussian process regression
David Burt, Carl Edward Rasmussen, and Mark Van Der Wilk · 2019
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Active regression via linear-sample sparsification
Xue Chen and Eric Price · 2019
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Fast determinantal point processes via distortion-free intermediate sampling
Michał Dereziński · 2019
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Exact sampling of determinantal point processes with sublinear time preprocessing
Michał Dereziński, Daniele Calandriello, and Michal Valko · 2019
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Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression
Michał Dereziński, Kenneth L. Clarkson, Michael W. Mahoney, and Manfred K. Warmuth · 2019
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Bayesian experimental design using regularized determinantal point processes
Michał Dereziński, Feynman Liang, and Michael W. Mahoney · 2019
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Exact expressions for double descent and implicit regularization via surrogate random design
Michał Dereziński, Feynman Liang, and Michael W. Mahoney · 2019
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Distributed estimation of the inverse Hessian by determinantal averaging
Michał Dereziński and Michael W Mahoney · 2019
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On two ways to use determinantal point processes for Monte Carlo integration
Guillaume Gautier, Rémi Bardenet, and Michal Valko · 2019
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Convergence Analysis of the Randomized Newton Method with Determinantal Sampling
Mojmír Mutný, Michał Dereziński, and Andreas Krause · 2019
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Michał Dereziński, Rajiv Khanna, and Michael W Mahoney · 2020
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Diversity sampling is an implicit regularization for kernel methods
Michaël Fanuel, Joachim Schreurs, and Johan AK Suykens · 2020
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High-performance sampling of generic determinantal point processes
Jack Poulson · 2020
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