暂无分享,去创建一个
Prateek Jain | Sham M. Kakade | Praneeth Netrapalli | Aaron Sidford | Rahul Kidambi | S. Kakade | Prateek Jain | Praneeth Netrapalli | Aaron Sidford | Rahul Kidambi
[1] E. L. Lehmann,et al. Theory of point estimation , 1950 .
[2] Boris Polyak. Some methods of speeding up the convergence of iteration methods , 1964 .
[3] Christopher C. Paige,et al. The computation of eigenvalues and eigenvectors of very large sparse matrices , 1971 .
[4] D. Anbar. On Optimal Estimation Methods Using Stochastic Approximation Procedures , 1973 .
[5] V. Fabian. Asymptotically Efficient Stochastic Approximation; The RM Case , 1973 .
[6] J. Proakis,et al. Channel identification for high speed digital communications , 1974 .
[7] G. Pflug. Stochastic Approximation Methods for Constrained and Unconstrained Systems - Kushner, HJ.; Clark, D.S. , 1980 .
[8] John Darzentas,et al. Problem Complexity and Method Efficiency in Optimization , 1983 .
[9] S. Thomas Alexander,et al. Adaptive Signal Processing , 1986, Texts and Monographs in Computer Science.
[10] D. Ruppert,et al. Efficient Estimations from a Slowly Convergent Robbins-Monro Process , 1988 .
[11] A. Greenbaum. Behavior of slightly perturbed Lanczos and conjugate-gradient recurrences , 1989 .
[12] John J. Shynk,et al. Analysis of the momentum LMS algorithm , 1990, IEEE Trans. Acoust. Speech Signal Process..
[13] Boris Polyak,et al. Acceleration of stochastic approximation by averaging , 1992 .
[14] O. Nelles,et al. An Introduction to Optimization , 1996, IEEE Antennas and Propagation Magazine.
[15] William A. Sethares,et al. Analysis of momentum adaptive filtering algorithms , 1998, IEEE Trans. Signal Process..
[16] H. Kushner,et al. Stochastic Approximation and Recursive Algorithms and Applications , 2003 .
[17] Yurii Nesterov,et al. Introductory Lectures on Convex Optimization - A Basic Course , 2014, Applied Optimization.
[18] H. Robbins. A Stochastic Approximation Method , 1951 .
[19] Léon Bottou,et al. The Tradeoffs of Large Scale Learning , 2007, NIPS.
[20] Alexandre d'Aspremont,et al. Smooth Optimization with Approximate Gradient , 2005, SIAM J. Optim..
[21] James T. Kwok,et al. Accelerated Gradient Methods for Stochastic Optimization and Online Learning , 2009, NIPS.
[22] Eric Moulines,et al. Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning , 2011, NIPS.
[23] Maxim Raginsky,et al. Information-Based Complexity, Feedback and Dynamics in Convex Programming , 2010, IEEE Transactions on Information Theory.
[24] Yurii Nesterov,et al. Efficiency of Coordinate Descent Methods on Huge-Scale Optimization Problems , 2012, SIAM J. Optim..
[25] Guanghui Lan,et al. An optimal method for stochastic composite optimization , 2011, Mathematical Programming.
[26] Saeed Ghadimi,et al. Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization I: A Generic Algorithmic Framework , 2012, SIAM J. Optim..
[27] Sham M. Kakade,et al. Random Design Analysis of Ridge Regression , 2012, COLT.
[28] Martin J. Wainwright,et al. Information-Theoretic Lower Bounds on the Oracle Complexity of Stochastic Convex Optimization , 2010, IEEE Transactions on Information Theory.
[29] Eric Moulines,et al. Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) , 2013, NIPS.
[30] Saeed Ghadimi,et al. Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization, II: Shrinking Procedures and Optimal Algorithms , 2013, SIAM J. Optim..
[31] Shai Shalev-Shwartz,et al. Accelerated Mini-Batch Stochastic Dual Coordinate Ascent , 2013, NIPS.
[32] Deanna Needell,et al. Stochastic gradient descent, weighted sampling, and the randomized Kaczmarz algorithm , 2013, Mathematical Programming.
[33] Jonathan D. Rosenblatt,et al. On the Optimality of Averaging in Distributed Statistical Learning , 2014, 1407.2724.
[34] F. Bach,et al. Non-parametric Stochastic Approximation with Large Step sizes , 2014, 1408.0361.
[35] Francis R. Bach,et al. Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression , 2013, J. Mach. Learn. Res..
[36] Yurii Nesterov,et al. First-order methods of smooth convex optimization with inexact oracle , 2013, Mathematical Programming.
[37] Francis R. Bach,et al. Averaged Least-Mean-Squares: Bias-Variance Trade-offs and Optimal Sampling Distributions , 2015, AISTATS.
[38] Sham M. Kakade,et al. Competing with the Empirical Risk Minimizer in a Single Pass , 2014, COLT.
[39] Sham M. Kakade,et al. Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization , 2015, ICML.
[40] Zaïd Harchaoui,et al. A Universal Catalyst for First-Order Optimization , 2015, NIPS.
[41] Nathan Srebro,et al. Tight Complexity Bounds for Optimizing Composite Objectives , 2016, NIPS.
[42] Ali H. Sayed,et al. On the influence of momentum acceleration on online learning , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[43] Prateek Jain,et al. Parallelizing Stochastic Approximation Through Mini-Batching and Tail-Averaging , 2016, ArXiv.
[44] Michael I. Jordan,et al. A Lyapunov Analysis of Momentum Methods in Optimization , 2016, ArXiv.
[45] Zeyuan Allen Zhu,et al. Katyusha: the first direct acceleration of stochastic gradient methods , 2017, STOC.