variance reduction

Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization ICML 2019 Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number ICML 2019 Marginal Policy Gradients: A Unified Family of Estimators for Bounded Action Spaces with Applications ICLR 2019 Minimal Variance Sampling in Stochastic Gradient Boosting NeurIPS 2019 Momentum-Based Variance Reduction in Non-Convex SGD NeurIPS 2019 More Efficient Estimation for Logistic Regression with Optimal Subsamples JMLR 2019 More Efficient Off-Policy Evaluation through Regularized Targeted Learning ICML 2019 Nonconvex Variance Reduced Optimization with Arbitrary Sampling ICML 2019 Online sampling from log-concave distributions NeurIPS 2019 Online Variance Reduction with Mixtures ICML 2019 On the Global Convergence of (Fast) Incremental Expectation Maximization Methods NeurIPS 2019 On the Ineffectiveness of Variance Reduced Optimization for Deep Learning NeurIPS 2019 Optimal Mini-Batch and Step Sizes for SAGA ICML 2019 Pathwise Derivatives for Multivariate Distributions AISTATS 2019 Proximal Splitting Meets Variance Reduction AISTATS 2019 Rao-Blackwellized Stochastic Gradients for Discrete Distributions ICML 2019 Reducing Noise in GAN Training with Variance Reduced Extragradient NeurIPS 2019 Reducing the variance in online optimization by transporting past gradients NeurIPS 2019 Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling NeurIPS 2019 SpiderBoost and Momentum: Faster Variance Reduction Algorithms NeurIPS 2019 Stabilized SVRG: Simple Variance Reduction for Nonconvex Optimization COLT 2019 Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction NeurIPS 2019 Stochastic Variance-Reduced Cubic Regularization Methods JMLR 2019 Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization NeurIPS 2019 Taming MAML: Efficient unbiased meta-reinforcement learning ICML 2019