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Kernel methods are fundamental in machine learning, and faster algorithms for kernel approximation provide direct speedups for many core tasks in machine learning.
An algorithm for the machine calculation of complex fourier series
James W Cooley and John W Tukey · 1965
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Jonathan Richard Shewchuk · 1994
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Craig Saunders, Alexander Gammerman, and Volodya Vovk · 1998
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A dimensionality reduction approach to modeling protein flexibility
Miguel L. Teodoro, G. Phillips, and L. Kavraki · 2002
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Tamas Sarlos · 2006
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Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
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splitSVM: Fast, space-efficient, non-heuristic, polynomial kernel computation for NLP applications
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Kernel methods for deep learning
Youngmin Cho and Lawrence K Saul · 2009
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Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard, and Chih-Jen Lin · 2010
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Multiplying matrices faster than coppersmith-winograd
Virginia Vassilevska Williams · 2012
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Sharp analysis of low-rank kernel matrix approximations
Francis Bach · 2013
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Low rank approximation and regression in input sparsity time
Kenneth L. Clarkson and David P. Woodruff · 2013
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Faster ridge regression via the subsampled randomized hadamard transform
Yichao Lu, Paramveer Dhillon, Dean P Foster, and Lyle Ungar · 2013
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Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression
Xiangrui Meng and Michael W Mahoney · 2013
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Osnap: Faster numerical linear algebra algorithms via sparser subspace embeddings
Jelani Nelson and Huy L Nguyên · 2013
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Subspace embeddings for the polynomial kernel
Haim Avron, Huy Nguyen, and David Woodruff · 2014
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Optimal cur matrix decompositions
Christos Boutsidis and David P Woodruff · 2014
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Powers of tensors and fast matrix multiplication
François Le Gall · 2014
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Sketching as a tool for numerical linear algebra
David P. Woodruff · 2014
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Fast randomized kernel ridge regression with statistical guarantees
Ahmed Alaoui and Michael W Mahoney · 2015
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An introduction to matrix concentration inequalities
Joel A Tropp · 2015
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Divide and conquer kernel ridge regression: A distributed algorithm with minimax optimal rates
Yuchen Zhang, John Duchi, and Martin Wainwright · 2015
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A convergence theory for deep learning via over-parameterization
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2019
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On the convergence rate of training recurrent neural networks
Zeyuan Allen-Zhu, Yuanzhi Li, and Zhao Song · 2019
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Optimal sketching for kronecker product regression and low rank approximation
Huaian Diao, Rajesh Jayaram, Zhao Song, Wen Sun, and David P Woodruff · 2019
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Gradient descent provably optimizes over-parameterized neural networks
Simon S Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh · 2019
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Solving empirical risk minimization in the current matrix multiplication time
Yin Tat Lee, Zhao Song, and Qiuyi Zhang · 2019
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Optimal principal component analysis in distributed and streaming models
Christos Boutsidis, David P Woodruff, and Peilin Zhong · 2016
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Preconditioning kernel matrices, 2016
Kurt Cutajar, Michael A. Osborne, John P. Cunningham, and Maurizio Filippone · 2016
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Sharper bounds for regularized data fitting
H. Avron, K. Clarkson, and D. Woodruff · 2017
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Faster kernel ridge regression using sketching and preconditioning
Haim Avron, Kenneth L Clarkson, and David P Woodruff · 2017
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Random fourier features for kernel ridge regression: Approximation bounds and statistical guarantees
Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, and Amir Zandieh · 2017
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Near optimal sketching of low-rank tensor regression
Jarvis Haupt, Xingguo Li, and David P Woodruff · 2017
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Zhao Song, David P Woodruff, and Peilin Zhong · 2019
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Quadratic suffices for over-parametrization via matrix chernoff bound
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Algorithms and hardness for linear algebra on geometric graphs
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Oblivious sketching of high-degree polynomial kernels
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Sparse principal component analysis for natural language processing
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An improved cutting plane method for convex optimization, convex-concave games and its applications
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Generalized leverage score sampling for neural networks
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Near input sparsity time kernel embeddings via adaptive sampling
David P Woodruff and Amir Zandieh · 2020
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Scaling up kernel ridge regression via locality sensitive hashing
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Training (overparametrized) neural networks in near-linear time
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Fl-ntk: A neural tangent kernel-based framework for federated learning convergence analysis
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Faster dynamic matrix inverse for faster lps
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A new initialization technique for reducing the width of neural networks
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Oblivious sketching-based central path method for solving linear programming problems
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