variance reduction

Multilevel Monte Carlo Variational Inference JMLR 2021 New Bounds For Distributed Mean Estimation and Variance Reduction ICLR 2021 Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning IJCAI 2021 On Estimation in Latent Variable Models ICML 2021 On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method NeurIPS 2021 On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients ICML 2021 Optimal Off-Policy Evaluation from Multiple Logging Policies ICML 2021 Posterior Value Functions: Hindsight Baselines for Policy Gradient Methods ICML 2021 Private Stochastic Convex Optimization: Optimal Rates in L1 Geometry ICML 2021 Provably Faster Algorithms for Bilevel Optimization NeurIPS 2021 Query complexity of least absolute deviation regression via robust uniform convergence COLT 2021 Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator ICLR 2021 Reserve Price Optimization for First Price Auctions in Display Advertising ICML 2021 Settling the Variance of Multi-Agent Policy Gradients NeurIPS 2021 Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions ICLR 2021 Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization NeurIPS 2021 State Relevance for Off-Policy Evaluation ICML 2021 Stochastic Anderson Mixing for Nonconvex Stochastic Optimization NeurIPS 2021 Stochastic Multi-Armed Bandits with Control Variates NeurIPS 2021 Stochastic Proximal AUC Maximization JMLR 2021 Storchastic: A Framework for General Stochastic Automatic Differentiation NeurIPS 2021 STORM+: Fully Adaptive SGD with Recursive Momentum for Nonconvex Optimization NeurIPS 2021 Strength in Numbers: Averaging and Clustering Effects in Mixture of Experts for Graph-Based Dependency Parsing ACL 2021 Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning NeurIPS 2021 The Power of Factorial Powers: New Parameter settings for (Stochastic) Optimization ACML 2021