Faster learning by reduction of data access time
暂无分享,去创建一个
[1] Tong Zhang,et al. Solving large scale linear prediction problems using stochastic gradient descent algorithms , 2004, ICML.
[2] Jorge Nocedal,et al. A Stochastic Quasi-Newton Method for Large-Scale Optimization , 2014, SIAM J. Optim..
[3] Siddharth Gopal,et al. Adaptive Sampling for SGD by Exploiting Side Information , 2016, ICML.
[4] Deanna Needell,et al. Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm , 2013, Mathematical Programming.
[5] Athanasios V. Vasilakos,et al. Machine learning on big data: Opportunities and challenges , 2017, Neurocomputing.
[6] Yoram Singer,et al. Pegasos: primal estimated sub-gradient solver for SVM , 2011, Math. Program..
[7] Tong Zhang,et al. Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling , 2014, ArXiv.
[8] Anuj Sharma,et al. Problem formulations and solvers in linear SVM: a review , 2018, Artificial Intelligence Review.
[9] Mark W. Schmidt,et al. Minimizing finite sums with the stochastic average gradient , 2013, Mathematical Programming.
[10] Anuj Sharma,et al. SAAGs: Biased Stochastic Variance Reduction Methods , 2018, ArXiv.
[11] Anuj Sharma,et al. Mini-batch Block-coordinate based Stochastic Average Adjusted Gradient Methods to Solve Big Data Problems , 2017, ACML.
[12] Wotao Yin,et al. Block Stochastic Gradient Iteration for Convex and Nonconvex Optimization , 2014, SIAM J. Optim..
[13] Quanquan Gu,et al. Accelerated Stochastic Block Coordinate Descent with Optimal Sampling , 2016, KDD.
[14] Sashank J. Reddi,et al. New Optimization Methods for Modern Machine Learning , 2017 .
[15] Peter Richtárik,et al. Coordinate Descent Face-Off: Primal or Dual? , 2016, 1605.08982.
[16] Francis Bach,et al. SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives , 2014, NIPS.
[17] Anuj Sharma,et al. Faster Algorithms for Large-scale Machine Learning using Simple Sampling Techniques , 2018, ArXiv.
[18] Peter Richtárik,et al. Randomized Dual Coordinate Ascent with Arbitrary Sampling , 2014, ArXiv.
[19] Ohad Shamir,et al. Better Mini-Batch Algorithms via Accelerated Gradient Methods , 2011, NIPS.
[20] Hongchao Zhang,et al. Inexact proximal stochastic gradient method for convex composite optimization , 2017, Comput. Optim. Appl..
[21] Alexander J. Smola,et al. Efficient mini-batch training for stochastic optimization , 2014, KDD.
[22] Chih-Jen Lin,et al. Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines , 2008, J. Mach. Learn. Res..
[23] Stephen J. Wright. Coordinate descent algorithms , 2015, Mathematical Programming.
[24] Tong Zhang,et al. Accelerating Stochastic Gradient Descent using Predictive Variance Reduction , 2013, NIPS.
[25] V VasilakosAthanasios,et al. Machine learning on big data , 2017 .
[26] Chih-Jen Lin,et al. Large Linear Classification When Data Cannot Fit in Memory , 2011, TKDD.
[27] Tong Zhang,et al. Stochastic Optimization with Importance Sampling for Regularized Loss Minimization , 2014, ICML.
[28] W. G. Madow. On the Theory of Systematic Sampling, II , 1944 .
[29] Avleen Singh Bijral,et al. Mini-Batch Primal and Dual Methods for SVMs , 2013, ICML.
[30] Alexander Shapiro,et al. Stochastic Approximation approach to Stochastic Programming , 2013 .
[31] W. G. Madow. On the Theory of Systematic Sampling, III. Comparison of Centered and Random Start Systematic Sampling , 1953 .