variance reduction

Starting Small - Learning with Adaptive Sample Sizes ICML 2016 Stochastic Block BFGS: Squeezing More Curvature out of Data ICML 2016 Stochastic Variance Reduced Optimization for Nonconvex Sparse Learning ICML 2016 Stochastic Variance Reduction for Nonconvex Optimization ICML 2016 Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation NeurIPS 2016 Variance-Reduced and Projection-Free Stochastic Optimization ICML 2016 Variance Reduction for Faster Non-Convex Optimization ICML 2016 Variance Reduction in Stochastic Gradient Langevin Dynamics NeurIPS 2016 Adaptive Strategy for Stratified Monte Carlo Sampling JMLR 2015 A Stochastic PCA and SVD Algorithm with an Exponential Convergence Rate ICML 2015 A Universal Catalyst for First-Order Optimization NeurIPS 2015 Fast Bidirectional Probability Estimation in Markov Models NeurIPS 2015 Gradient Estimation Using Stochastic Computation Graphs NeurIPS 2015 Local Smoothness in Variance Reduced Optimization NeurIPS 2015 On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants NeurIPS 2015 Regularized Policy Gradients: Direct Variance Reduction in Policy Gradient Estimation ACML 2015 Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems NeurIPS 2015 Stochastic Optimization with Importance Sampling for Regularized Loss Minimization ICML 2015 StopWasting My Gradients: Practical SVRG NeurIPS 2015 Variance Reduced Stochastic Gradient Descent with Neighbors NeurIPS 2015 Variance Reduction via Antithetic Markov Chains AISTATS 2015 Accelerated Mini-batch Randomized Block Coordinate Descent Method NeurIPS 2014 Neural Variational Inference and Learning in Belief Networks ICML 2014 Smoothed Gradients for Stochastic Variational Inference NeurIPS 2014 Stochastic Proximal Gradient Descent with Acceleration Techniques NeurIPS 2014