Learning with SGD and Random Features

17 July 2018

Papers citing "Learning with SGD and Random Features"

29 / 29 papers shown

Title
Optimal Kernel Quantile Learning with Random Features Caixing Wang Xingdong Feng 64 1 0 24 Aug 2024
On Fast Leverage Score Sampling and Optimal Learning Alessandro Rudi Daniele Calandriello Luigi Carratino Lorenzo Rosasco 34 81 0 31 Oct 2018
Localized Structured Prediction C. Ciliberto Francis R. Bach Alessandro Rudi 28 28 0 06 Jun 2018
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes Loucas Pillaud-Vivien Alessandro Rudi Francis R. Bach 68 100 0 25 May 2018
Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces Junhong Lin Alessandro Rudi Lorenzo Rosasco Volkan Cevher 64 99 0 20 Jan 2018
Exponential convergence of testing error for stochastic gradient methods Loucas Pillaud-Vivien Alessandro Rudi Francis R. Bach 55 31 0 13 Dec 2017
Optimal Rates for Learning with Nyström Stochastic Gradient Methods Junhong Lin Lorenzo Rosasco 66 7 0 21 Oct 2017
Generalization Properties of Doubly Stochastic Learning Algorithms Junhong Lin Lorenzo Rosasco 44 7 0 03 Jul 2017
FALKON: An Optimal Large Scale Kernel Method Alessandro Rudi Luigi Carratino Lorenzo Rosasco 34 196 0 31 May 2017
Consistent Multitask Learning with Nonlinear Output Relations C. Ciliberto Alessandro Rudi Lorenzo Rosasco Massimiliano Pontil 41 33 0 23 May 2017
On Structured Prediction Theory with Calibrated Convex Surrogate Losses A. Osokin Francis R. Bach Simon Lacoste-Julien 44 61 0 07 Mar 2017
Orthogonal Random Features Felix X. Yu A. Suresh K. Choromanski D. Holtmann-Rice Sanjiv Kumar 63 221 0 28 Oct 2016
A Consistent Regularization Approach for Structured Prediction C. Ciliberto Alessandro Rudi Lorenzo Rosasco 66 79 0 24 May 2016
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression Aymeric Dieuleveut Nicolas Flammarion Francis R. Bach ODL 22 225 0 17 Feb 2016
Large Scale Kernel Learning using Block Coordinate Descent Stephen Tu Rebecca Roelofs Shivaram Venkataraman Benjamin Recht 30 42 0 17 Feb 2016
Generalization Properties of Learning with Random Features Alessandro Rudi Lorenzo Rosasco MLT 56 329 0 14 Feb 2016
NYTRO: When Subsampling Meets Early Stopping Tomás Angles Raffaello Camoriano Alessandro Rudi Lorenzo Rosasco 43 32 0 19 Oct 2015
Less is More: Nyström Computational Regularization Alessandro Rudi Raffaello Camoriano Lorenzo Rosasco 31 277 0 16 Jul 2015
Optimal Rates for Random Fourier Features Bharath K. Sriperumbudur Z. Szabó 45 130 0 06 Jun 2015
Randomized sketches for kernels: Fast and optimal non-parametric regression Yun Yang Mert Pilanci Martin J. Wainwright 30 174 0 25 Jan 2015
On the Sample Complexity of Subspace Learning Alessandro Rudi Guillermo D. Cañas Lorenzo Rosasco 31 29 0 21 Aug 2014
Fastfood: Approximate Kernel Expansions in Loglinear Time Quoc V. Le Tamás Sarlós Alex Smola 60 442 0 13 Aug 2014
Non-parametric Stochastic Approximation with Large Step sizes Aymeric Dieuleveut Francis R. Bach 37 169 0 02 Aug 2014
Scalable Kernel Methods via Doubly Stochastic Gradients Bo Dai Bo Xie Niao He Yingyu Liang Anant Raj Maria-Florina Balcan Le Song 109 230 0 21 Jul 2014
Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning Francesco Orabona 127 103 0 15 Jun 2014
Compact Random Feature Maps Raffay Hamid Ying Xiao Alex Gittens D. DeCoste 53 86 0 17 Dec 2013
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes Ohad Shamir Tong Zhang 132 573 0 08 Dec 2012
Fast approximation of matrix coherence and statistical leverage P. Drineas M. Magdon-Ismail Michael W. Mahoney David P. Woodruff 124 531 0 18 Sep 2011
Optimal Distributed Online Prediction using Mini-Batches O. Dekel Ran Gilad-Bachrach Ohad Shamir Lin Xiao 241 683 0 07 Dec 2010