StopWasting My Gradients: Practical SVRG
暂无分享,去创建一个
Mark W. Schmidt | Mohamed Osama Ahmed | Scott Sallinen | Jakub Konecný | Reza Babanezhad | Alim Virani
[1] Peter Richtárik,et al. Semi-Stochastic Gradient Descent Methods , 2013, Front. Appl. Math. Stat..
[2] Thorsten Joachims,et al. Making large scale SVM learning practical , 1998 .
[3] S. Rosset,et al. Piecewise linear regularized solution paths , 2007, 0708.2197.
[4] Uri M. Ascher,et al. Adaptive and Stochastic Algorithms for Electrical Impedance Tomography and DC Resistivity Problems with Piecewise Constant Solutions and Many Measurements , 2012, SIAM J. Sci. Comput..
[5] Mark W. Schmidt,et al. Hybrid Deterministic-Stochastic Methods for Data Fitting , 2011, SIAM J. Sci. Comput..
[6] Jie Liu,et al. Mini-Batch Semi-Stochastic Gradient Descent in the Proximal Setting , 2015, IEEE Journal of Selected Topics in Signal Processing.
[7] Léon Bottou,et al. The Tradeoffs of Large Scale Learning , 2007, NIPS.
[8] Felix J. Herrmann,et al. Robust inversion, dimensionality reduction, and randomized sampling , 2012, Math. Program..
[9] Yurii Nesterov,et al. Introductory Lectures on Convex Optimization - A Basic Course , 2014, Applied Optimization.
[10] James T. Kwok,et al. Accelerated Gradient Methods for Stochastic Optimization and Online Learning , 2009, NIPS.
[11] Andreas Krause,et al. Advances in Neural Information Processing Systems (NIPS) , 2014 .
[12] Antoine Bordes,et al. Guarantees for Approximate Incremental SVMs , 2010, AISTATS.
[13] Francis Bach,et al. SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives , 2014, NIPS.
[14] Gordon V. Cormack,et al. Spam Corpus Creation for TREC , 2005, CEAS.
[15] U. Ascher,et al. Adaptive and stochastic algorithms for EIT and DC resistivity problems with piecewise constant solutions and many measurements , 2011 .
[16] Léon Bottou,et al. Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.
[17] Jorge Nocedal,et al. Sample size selection in optimization methods for machine learning , 2012, Math. Program..
[18] Tong Zhang,et al. Accelerating Stochastic Gradient Descent using Predictive Variance Reduction , 2013, NIPS.
[19] Rong Jin,et al. Mixed Optimization for Smooth Functions , 2013, NIPS.
[20] Lin Xiao,et al. A Proximal Stochastic Gradient Method with Progressive Variance Reduction , 2014, SIAM J. Optim..
[21] Thorsten Joachims,et al. KDD-Cup 2004: results and analysis , 2004, SKDD.
[22] Sharon L. Lohr,et al. Sampling: Design and Analysis , 1999 .
[23] Mark W. Schmidt,et al. A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets , 2012, ArXiv.
[24] Julien Mairal,et al. Optimization with First-Order Surrogate Functions , 2013, ICML.
[25] Peter Carbonetto,et al. New probabilistic inference algorithms that harness the strengths of variational and Monte Carlo methods , 2009 .
[26] Yiming Yang,et al. RCV1: A New Benchmark Collection for Text Categorization Research , 2004, J. Mach. Learn. Res..
[27] Rong Jin,et al. Linear Convergence with Condition Number Independent Access of Full Gradients , 2013, NIPS.
[28] Shai Shalev-Shwartz,et al. Stochastic dual coordinate ascent methods for regularized loss , 2012, J. Mach. Learn. Res..
[29] S. Sathiya Keerthi,et al. A Modified Finite Newton Method for Fast Solution of Large Scale Linear SVMs , 2005, J. Mach. Learn. Res..
[30] Mark W. Schmidt,et al. Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization , 2011, NIPS.