stochastic variance reduction

Efficient Continual Finite-Sum Minimization ICLR 2024 Near-Optimal Distributed Minimax Optimization under the Second-Order Similarity NeurIPS 2024 Scalable Optimal Margin Distribution Machine IJCAI 2023 Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization NeurIPS 2022 Kill a Bird with Two Stones: Closing the Convergence Gaps in Non-Strongly Convex Optimization by Directly Accelerated SVRG with Double Compensation and Snapshots ICML 2022 Asynchronous Decentralized Optimization With Implicit Stochastic Variance Reduction ICML 2021 The Power of Factorial Powers: New Parameter settings for (Stochastic) Optimization ACML 2021 An Effective Hard Thresholding Method Based on Stochastic Variance Reduction for Nonconvex Sparse Learning AAAI 2020 History-Gradient Aided Batch Size Adaptation for Variance Reduced Algorithms ICML 2020 An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient UAI 2019 Proximal Splitting Meets Variance Reduction AISTATS 2019 SpiderBoost and Momentum: Faster Variance Reduction Algorithms NeurIPS 2019 Stochastic Variance-Reduced Cubic Regularization for Nonconvex Optimization AISTATS 2019 Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization NeurIPS 2019 Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs ICML 2018 Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis AISTATS 2018 Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction NeurIPS 2017 Stochastic Variance Reduction Methods for Policy Evaluation ICML 2017 Stochastic Variance Reduction for Nonconvex Optimization ICML 2016 Stochastic Variance Reduction Methods for Saddle-Point Problems NeurIPS 2016 Tight Complexity Bounds for Optimizing Composite Objectives NeurIPS 2016