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We consider the problem of improving kernel approximation via randomized feature maps.
Monotone funktionen, stieltjessche integrale und harmonische analyse
Salomon Bochner · 1933
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The efficient generation of random orthogonal matrices with an application to condition estimators
G. W. Stewart · 1980
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Generation of random orthogonal matrices
Theodore W Anderson, Ingram Olkin, and Les G Underhill · 1987
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Integration over spheres and the divergence theorem for balls
John A Baker · 1997
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Some methods for generating both an NT-net and the uniform distribution on a Stiefel manifold and their applications
Kai-Tai Fang and Run-Ze Li · 1997
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Spherical-radial integration rules for bayesian computation
John Monahan and Alan Genz · 1997
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Computing with infinite networks
Christopher KI Williams · 1997
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Methods for generating random orthogonal matrices
Alan Genz · 1998
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Stochastic integration rules for infinite regions
Alan Genz and John Monahan · 1998
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Latin supercube sampling for very high-dimensional simulations
Art B Owen · 1998
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A stochastic algorithm for high-dimensional integrals over unbounded regions with gaussian weight
Alan Genz and John Monahan · 1999
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Molecular classification of cancer: class discovery and class prediction by gene expression monitoring
Todd R Golub, Donna K Slonim, Pablo Tamayo, Christine Huard, Michelle Gaasenbeek, Jill P Mesirov, Hilary Coller, Mignon L Loh, James R Downing, Mark A Caligiuri, et al · 1999
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Sparse greedy matrix approximation for machine learning
Alex J Smola and Bernhard Schölkopf · 2000
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Efficient SVM training using low-rank kernel representations
Shai Fine and Katya Scheinberg · 2001
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On the Nyström method for approximating a Gram matrix for improved kernel-based learning
Petros Drineas and Michael W Mahoney · 2005
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How to generate random matrices from the classical compact groups
Francesco Mezzadri · 2006
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Engineering design via surrogate modelling: a practical guide
Alexander Forrester, Andy Keane, et al · 2008
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Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2008
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Kernel methods match deep neural networks on timit
Po-Sen Huang, Haim Avron, Tara N Sainath, Vikas Sindhwani, and Bhuvana Ramabhadran · 2014
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Quasi-Monte Carlo feature maps for shift-invariant kernels
Jiyan Yang, Vikas Sindhwani, Haim Avron, and Michael Mahoney · 2014
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Training-efficient feature map for shift-invariant kernels
Xixian Chen, Haiqin Yang, Irwin King, and Michael R Lyu · 2015
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On the error of random fourier features
Dougal J Sutherland and Jeff Schneider · 2015
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Compact nonlinear maps and circulant extensions
Felix X Yu, Sanjiv Kumar, Henry Rowley, and Shih-Fu Chang · 2015
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Orthogonal Random Features
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On the equivalence between kernel quadrature rules and random feature expansions
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The unreasonable effectiveness of random orthogonal embeddings
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Gaussian quadrature for kernel features
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