variance reduction

Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox COLT 2017 Mini-batch Block-coordinate based Stochastic Average Adjusted Gradient Methods to Solve Big Data Problems ACML 2017 Non-convex Finite-Sum Optimization Via SCSG Methods NeurIPS 2017 Nonlinear Acceleration of Stochastic Algorithms NeurIPS 2017 REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models NeurIPS 2017 Reducing Reparameterization Gradient Variance NeurIPS 2017 Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms AISTATS 2017 Safe Adaptive Importance Sampling NeurIPS 2017 SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient ICML 2017 Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference NeurIPS 2017 Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure NeurIPS 2017 Zap Q-Learning NeurIPS 2017 Accelerated Stochastic Gradient Descent for Minimizing Finite Sums AISTATS 2016 Adaptive Sampling for SGD by Exploiting Side Information ICML 2016 A Fast and Reliable Policy Improvement Algorithm AISTATS 2016 A Linearly-Convergent Stochastic L-BFGS Algorithm AISTATS 2016 Bootstrap Model Aggregation for Distributed Statistical Learning NeurIPS 2016 Control Functionals for Quasi-Monte Carlo Integration AISTATS 2016 Cyclades: Conflict-free Asynchronous Machine Learning NeurIPS 2016 Doubly Robust Off-policy Value Evaluation for Reinforcement Learning ICML 2016 Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters NeurIPS 2016 Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics JMLR 2016 LazySVD: Even Faster SVD Decomposition Yet Without Agonizing Pain NeurIPS 2016 Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization NeurIPS 2016 Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds NeurIPS 2016