variance reduction

Tilted Empirical Risk Minimization ICLR 2021 Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods NeurIPS 2021 Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits JMLR 2021 Variance-Aware Off-Policy Evaluation with Linear Function Approximation NeurIPS 2021 Variance Penalized On-Policy and Off-Policy Actor-Critic AAAI 2021 Variance-reduced First-order Meta-learning for Natural Language Processing Tasks NAACL 2021 Variance Reduced Median-of-Means Estimator for Byzantine-Robust Distributed Inference JMLR 2021 Variance Reduced Training with Stratified Sampling for Forecasting Models ICML 2021 Variance reduction in frequency estimators via control variates method UAI 2021 Variance Reduction via Primal-Dual Accelerated Dual Averaging for Nonsmooth Convex Finite-Sums ICML 2021 A Catalyst Framework for Minimax Optimization NeurIPS 2020 Accelerated Stochastic Gradient-free and Projection-free Methods ICML 2020 Accelerating Stratified Sampling SGD by Reconstructing Strata IJCAI 2020 Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization ICML 2020 A Characterization of Mean Squared Error for Estimator with Bagging AISTATS 2020 Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation ICLR 2020 Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes NeurIPS 2020 Adaptive Trade-Offs in Off-Policy Learning AISTATS 2020 A Group-Theoretic Framework for Data Augmentation NeurIPS 2020 A Group-Theoretic Framework for Data Augmentation JMLR 2020 A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning AISTATS 2020 Almost Tune-Free Variance Reduction ICML 2020 Amortized Nesterov’s Momentum: A Robust Momentum and Its Application to Deep Learning UAI 2020 Amortized variance reduction for doubly stochastic objective UAI 2020 An Õ(m/ε^3.5)-Cost Algorithm for Semidefinite Programs with Diagonal Constraints COLT 2020