Unifying mirror descent and dual averaging
暂无分享,去创建一个
[1] András György,et al. A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds , 2017, ALT.
[2] Alexander V. Nazin,et al. Algorithms of Inertial Mirror Descent in Convex Problems of Stochastic Optimization , 2017, Automation and Remote Control.
[3] Tamir Hazan,et al. Tight Bounds for Bandit Combinatorial Optimization , 2017, COLT.
[4] Francis R. Bach,et al. Stochastic Composite Least-Squares Regression with Convergence Rate $O(1/n)$ , 2017, COLT.
[5] John C. Duchi,et al. Asymptotic optimality in stochastic optimization , 2016, The Annals of Statistics.
[6] Elad Hazan,et al. Introduction to Online Convex Optimization , 2016, Found. Trends Optim..
[7] Alexandre M. Bayen,et al. Accelerated Mirror Descent in Continuous and Discrete Time , 2015, NIPS.
[8] Yu. Nesterov,et al. Quasi-monotone Subgradient Methods for Nonsmooth Convex Minimization , 2015, J. Optim. Theory Appl..
[9] Zeyuan Allen Zhu,et al. Linear Coupling: An Ultimate Unification of Gradient and Mirror Descent , 2014, ITCS.
[10] Sébastien Bubeck,et al. Convex Optimization: Algorithms and Complexity , 2014, Found. Trends Mach. Learn..
[11] H. Brendan McMahan,et al. A survey of Algorithms and Analysis for Adaptive Online Learning , 2014, J. Mach. Learn. Res..
[12] Panayotis Mertikopoulos,et al. A continuous-time approach to online optimization , 2014, Journal of Dynamics & Games.
[13] Koby Crammer,et al. A generalized online mirror descent with applications to classification and regression , 2013, Machine Learning.
[14] Arkadi Nemirovski,et al. Dual subgradient algorithms for large-scale nonsmooth learning problems , 2013, Math. Program..
[15] Sanjoy Dasgupta,et al. Agglomerative Bregman Clustering , 2012, ICML.
[16] Guanghui Lan,et al. An optimal method for stochastic composite optimization , 2011, Mathematical Programming.
[17] Sébastien Bubeck,et al. Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems , 2012, Found. Trends Mach. Learn..
[18] Gábor Lugosi,et al. Regret in Online Combinatorial Optimization , 2012, Math. Oper. Res..
[19] Shai Shalev-Shwartz,et al. Online Learning and Online Convex Optimization , 2012, Found. Trends Mach. Learn..
[20] Stephen J. Wright,et al. Manifold Identification in Dual Averaging for Regularized Stochastic Online Learning , 2012, J. Mach. Learn. Res..
[21] Sham M. Kakade,et al. Towards Minimax Policies for Online Linear Optimization with Bandit Feedback , 2012, COLT.
[22] H. Brendan McMahan,et al. Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization , 2011, AISTATS.
[23] Yoram Singer,et al. Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..
[24] Ohad Shamir,et al. Optimal Distributed Online Prediction Using Mini-Batches , 2010, J. Mach. Learn. Res..
[25] Martin J. Wainwright,et al. Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling , 2010, IEEE Transactions on Automatic Control.
[26] Jean-Yves Audibert,et al. Regret Bounds and Minimax Policies under Partial Monitoring , 2010, J. Mach. Learn. Res..
[27] Matthew J. Streeter,et al. Adaptive Bound Optimization for Online Convex Optimization , 2010, COLT 2010.
[28] Lin Xiao,et al. Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization , 2009, J. Mach. Learn. Res..
[29] Jean-Yves Audibert,et al. Minimax Policies for Adversarial and Stochastic Bandits. , 2009, COLT 2009.
[30] Elad Hazan,et al. Extracting certainty from uncertainty: regret bounded by variation in costs , 2008, Machine Learning.
[31] Alexander Shapiro,et al. Stochastic Approximation approach to Stochastic Programming , 2013 .
[32] A. Juditsky,et al. Solving variational inequalities with Stochastic Mirror-Prox algorithm , 2008, 0809.0815.
[33] Peter L. Bartlett,et al. Adaptive Online Gradient Descent , 2007, NIPS.
[34] Yurii Nesterov,et al. Dual extrapolation and its applications to solving variational inequalities and related problems , 2003, Math. Program..
[35] Gábor Lugosi,et al. Prediction, learning, and games , 2006 .
[36] A. Juditsky,et al. Learning by mirror averaging , 2005, math/0511468.
[37] Yurii Nesterov,et al. Primal-dual subgradient methods for convex problems , 2005, Math. Program..
[38] Y. Mansour,et al. Improved second-order bounds for prediction with expert advice , 2005, Machine Learning.
[39] Alexander V. Nazin,et al. Recursive Aggregation of Estimators by the Mirror Descent Algorithm with Averaging , 2005, Probl. Inf. Transm..
[40] Yurii Nesterov,et al. Smooth minimization of non-smooth functions , 2005, Math. Program..
[41] Martin Zinkevich,et al. Online Convex Programming and Generalized Infinitesimal Gradient Ascent , 2003, ICML.
[42] Marc Teboulle,et al. Mirror descent and nonlinear projected subgradient methods for convex optimization , 2003, Oper. Res. Lett..
[43] Marc Teboulle,et al. Convergence Analysis of a Proximal-Like Minimization Algorithm Using Bregman Functions , 1993, SIAM J. Optim..
[44] A. Goldstein. Convex programming in Hilbert space , 1964 .
[45] Elad Hazan. The convex optimization approach to regret minimization , 2011 .
[46] Marc Teboulle,et al. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..
[47] Shai Shalev-Shwartz,et al. Online learning: theory, algorithms and applications (למידה מקוונת.) , 2007 .
[48] Arkadi Nemirovski,et al. Prox-Method with Rate of Convergence O(1/t) for Variational Inequalities with Lipschitz Continuous Monotone Operators and Smooth Convex-Concave Saddle Point Problems , 2004, SIAM J. Optim..
[49] John Darzentas,et al. Problem Complexity and Method Efficiency in Optimization , 1983 .
[50] L. Bregman. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming , 1967 .
[51] Boris Polyak,et al. Constrained minimization methods , 1966 .