strongly convex

On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo ICML 2018 SEGA: Variance Reduction via Gradient Sketching NeurIPS 2018 SGD and Hogwild! Convergence Without the Bounded Gradients Assumption ICML 2018 Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization NeurIPS 2017 Online Learning with a Hint NeurIPS 2017 Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure NeurIPS 2017 The Sample Complexity of Optimizing a Convex Function COLT 2017 A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights JMLR 2016 An Improved Convergence Analysis of Cyclic Block Coordinate Descent-type Methods for Strongly Convex Minimization AISTATS 2016 A Simple Practical Accelerated Method for Finite Sums NeurIPS 2016 Barzilai-Borwein Step Size for Stochastic Gradient Descent NeurIPS 2016 A General Analysis of the Convergence of ADMM ICML 2015 A Lower Bound for the Optimization of Finite Sums ICML 2015 An Accelerated Proximal Coordinate Gradient Method NeurIPS 2014 Accelerating Stochastic Gradient Descent using Predictive Variance Reduction NeurIPS 2013 Beating Bandits in Gradually Evolving Worlds COLT 2013 O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex Functions ICML 2013 Passive Learning with Target Risk COLT 2013 Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes ICML 2013 A Stochastic Gradient Method with an Exponential Convergence _Rate for Finite Training Sets NeurIPS 2012 Differentially Private Online Learning COLT 2012 Open Problem: Is Averaging Needed for Strongly Convex Stochastic Gradient Descent? COLT 2012 Optimal Regularized Dual Averaging Methods for Stochastic Optimization NeurIPS 2012 Regularization Techniques for Learning with Matrices JMLR 2012 Stochastic Gradient Descent with Only One Projection NeurIPS 2012