High probability guarantees for stochastic convex optimization
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[1] Alexander V. Nazin,et al. Algorithms of Robust Stochastic Optimization Based on Mirror Descent Method , 2019, Automation and Remote Control.
[2] Sham M. Kakade,et al. The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure , 2019, NeurIPS.
[3] Julien Mairal,et al. Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise , 2019, J. Mach. Learn. Res..
[4] Rong Jin,et al. Why Does Stagewise Training Accelerate Convergence of Testing Error Over SGD? , 2018, ArXiv.
[5] Yurii Nesterov,et al. Lectures on Convex Optimization , 2018 .
[6] Kannan Ramchandran,et al. Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates , 2018, ICML.
[7] Zeyuan Allen-Zhu,et al. How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGD , 2018, NeurIPS.
[8] Amir Beck,et al. First-Order Methods in Optimization , 2017 .
[9] Lili Su,et al. Distributed Statistical Machine Learning in Adversarial Settings: Byzantine Gradient Descent , 2019, PERV.
[10] G. Lugosi,et al. Sub-Gaussian estimators of the mean of a random vector , 2017, The Annals of Statistics.
[11] G. Lugosi,et al. Risk minimization by median-of-means tournaments , 2016, Journal of the European Mathematical Society.
[12] G. Lugosi,et al. On the estimation of the mean of a random vector , 2016, 1607.05421.
[13] A. Juditsky,et al. Deterministic and Stochastic Primal-Dual Subgradient Algorithms for Uniformly Convex Minimization , 2014 .
[14] Saeed Ghadimi,et al. Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization, II: Shrinking Procedures and Optimal Algorithms , 2013, SIAM J. Optim..
[15] Stanislav Minsker. Geometric median and robust estimation in Banach spaces , 2013, 1308.1334.
[16] Daniel J. Hsu,et al. Loss Minimization and Parameter Estimation with Heavy Tails , 2013, J. Mach. Learn. Res..
[17] Saeed Ghadimi,et al. Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization I: A Generic Algorithmic Framework , 2012, SIAM J. Optim..
[18] Nicolò Cesa-Bianchi,et al. Bandits With Heavy Tail , 2012, IEEE Transactions on Information Theory.
[19] O. Catoni. Challenging the empirical mean and empirical variance: a deviation study , 2010, 1009.2048.
[20] Alexander Shapiro,et al. Stochastic Approximation approach to Stochastic Programming , 2013 .
[21] Peter L. Bartlett,et al. Rademacher and Gaussian Complexities: Risk Bounds and Structural Results , 2003, J. Mach. Learn. Res..
[22] André Elisseeff,et al. Stability and Generalization , 2002, J. Mach. Learn. Res..
[23] Yurii Nesterov,et al. Confidence level solutions for stochastic programming , 2000, Autom..
[24] Noga Alon,et al. The space complexity of approximating the frequency moments , 1996, STOC '96.
[25] Boris Polyak,et al. Acceleration of stochastic approximation by averaging , 1992 .
[26] Leslie G. Valiant,et al. Random Generation of Combinatorial Structures from a Uniform Distribution , 1986, Theor. Comput. Sci..
[27] R. Rockafellar. Monotone Operators and the Proximal Point Algorithm , 1976 .
[28] Alon Gonen. Understanding Machine Learning From Theory to Algorithms 1st Edition Shwartz Solutions Manual , 2015 .
[29] Ohad Shamir,et al. Stochastic Convex Optimization , 2009, COLT.
[30] John Darzentas,et al. Problem Complexity and Method Efficiency in Optimization , 1983 .
[31] B. Martinet,et al. R'egularisation d''in'equations variationnelles par approximations successives , 1970 .
[32] Elad Hazan. 24th Annual Conference on Learning Theory Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization , 2022 .