Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning
暂无分享,去创建一个
[1] V. Fabian. On Asymptotic Normality in Stochastic Approximation , 1968 .
[2] John Darzentas,et al. Problem Complexity and Method Efficiency in Optimization , 1983 .
[3] N. Vakhania,et al. Probability Distributions on Banach Spaces , 1987 .
[4] D. Ruppert,et al. Efficient Estimations from a Slowly Convergent Robbins-Monro Process , 1988 .
[5] R. Durrett. Probability: Theory and Examples , 1993 .
[6] Boris Polyak,et al. Acceleration of stochastic approximation by averaging , 1992 .
[7] C. Ahlbrandt,et al. Discrete Hamiltonian Systems: Difference Equations, Continued Fractions, and Riccati Equations , 1996 .
[8] Alexander J. Smola,et al. Learning with kernels , 1998 .
[9] J. Borwein,et al. Convex Analysis And Nonlinear Optimization , 2000 .
[10] Ingo Steinwart,et al. On the Influence of the Kernel on the Consistency of Support Vector Machines , 2002, J. Mach. Learn. Res..
[11] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2003, ICTAI.
[12] H. Kushner,et al. Stochastic Approximation and Recursive Algorithms and Applications , 2003 .
[13] Yurii Nesterov,et al. Introductory Lectures on Convex Optimization - A Basic Course , 2014, Applied Optimization.
[14] Léon Bottou,et al. On-line learning for very large data sets: Research Articles , 2005 .
[15] Léon Bottou,et al. On-line learning for very large data sets , 2005 .
[16] Léon Bottou,et al. The Tradeoffs of Large Scale Learning , 2007, NIPS.
[17] Yoram Singer,et al. Pegasos: primal estimated sub-gradient solver for SVM , 2007, ICML '07.
[18] Nathan Srebro,et al. SVM optimization: inverse dependence on training set size , 2008, ICML '08.
[19] Nathan Srebro,et al. Fast Rates for Regularized Objectives , 2008, NIPS.
[20] Yurii Nesterov,et al. Confidence level solutions for stochastic programming , 2000, Autom..
[21] Lin Xiao,et al. Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization , 2009, J. Mach. Learn. Res..
[22] Martin J. Wainwright,et al. Information-theoretic lower bounds on the oracle complexity of convex optimization , 2009, NIPS.
[23] Francis R. Bach,et al. Self-concordant analysis for logistic regression , 2009, ArXiv.
[24] Alexander Shapiro,et al. Stochastic Approximation approach to Stochastic Programming , 2013 .
[25] Yoram Singer,et al. Efficient Online and Batch Learning Using Forward Backward Splitting , 2009, J. Mach. Learn. Res..
[26] Ohad Shamir,et al. Stochastic Convex Optimization , 2009, COLT.
[27] Mark Broadie,et al. General Bounds and Finite-Time Improvement for the Kiefer-Wolfowitz Stochastic Approximation Algorithm , 2011, Oper. Res..
[28] Elad Hazan,et al. An optimal algorithm for stochastic strongly-convex optimization , 2010, 1006.2425.
[29] Yoram Singer,et al. Pegasos: primal estimated sub-gradient solver for SVM , 2011, Math. Program..
[30] Martin J. Wainwright,et al. Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization , 2010, IEEE Transactions on Information Theory.