finite-sum optimization

Variance Reduced Coordinate Descent with Acceleration: New Method With a Surprising Application to Finite-Sum Problems ICML 2020 Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization NeurIPS 2020 An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums NeurIPS 2019 A unified variance-reduced accelerated gradient method for convex optimization NeurIPS 2019 Lower Bounds for Smooth Nonconvex Finite-Sum Optimization ICML 2019 SAGA with Arbitrary Sampling ICML 2019 Breaking the Span Assumption Yields Fast Finite-Sum Minimization NeurIPS 2018 Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs ICML 2018 Katyusha: The First Direct Acceleration of Stochastic Gradient Methods JMLR 2018 Level-Set Methods for Finite-Sum Constrained Convex Optimization ICML 2018 SGDLibrary: A MATLAB library for stochastic optimization algorithms JMLR 2018 Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization NeurIPS 2017 Non-convex Finite-Sum Optimization Via SCSG Methods NeurIPS 2017 Oracle Complexity of Second-Order Methods for Finite-Sum Problems ICML 2017 A Simple Practical Accelerated Method for Finite Sums NeurIPS 2016 A Superlinearly-Convergent Proximal Newton-type Method for the Optimization of Finite Sums ICML 2016 Dimension-Free Iteration Complexity of Finite Sum Optimization Problems NeurIPS 2016 Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization NeurIPS 2016 A Lower Bound for the Optimization of Finite Sums ICML 2015