variance reduction

SteinDreamer: Variance Reduction for Text-to-3D Score Distillation via Stein Identity AISTATS 2025 Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees AISTATS 2025 Variance-Reduced Forward-Reflected-Backward Splitting Methods for Nonmonotone Generalized Equations ICML 2025 Variance-Reducing Couplings for Random Features ICLR 2025 VFOSA: Variance-Reduced Fast Operator Splitting Algorithms for Generalized Equations JMLR 2025 When Extragradient Meets PAGE: Bridging Two Giants to Boost Variational Inequalities UAI 2025 Zeroth-Order Methods for Nonconvex Stochastic Problems with Decision-Dependent Distributions AAAI 2025 A Bregman Proximal Stochastic Gradient Method with Extrapolation for Nonconvex Nonsmooth Problems AAAI 2024 Adaptive Exploration for Data-Efficient General Value Function Evaluations NeurIPS 2024 Adaptive Federated Minimax Optimization with Lower Complexities AISTATS 2024 Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions NeurIPS 2024 A Homogenization Approach for Gradient-Dominated Stochastic Optimization UAI 2024 An Effective Dynamic Gradient Calibration Method for Continual Learning ICML 2024 Averaging n-step Returns Reduces Variance in Reinforcement Learning ICML 2024 Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems AISTATS 2024 Byzantine Robustness and Partial Participation Can Be Achieved at Once: Just Clip Gradient Differences NeurIPS 2024 Complexity of Single Loop Algorithms for Nonlinear Programming with Stochastic Objective and Constraints AISTATS 2024 Decentralized Gradient-Free Methods for Stochastic Non-smooth Non-convex Optimization AAAI 2024 Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learning JMLR 2024 Decentralized Sum-of-Nonconvex Optimization AAAI 2024 Diagonalisation SGD: Fast & Convergent SGD for Non-Differentiable Models via Reparameterisation and Smoothing AISTATS 2024 DISCount: Counting in Large Image Collections with Detector-Based Importance Sampling AAAI 2024 Don't Compress Gradients in Random Reshuffling: Compress Gradient Differences NeurIPS 2024 Double Variance Reduction: A Smoothing Trick for Composite Optimization Problems without First-Order Gradient ICML 2024 Drago: Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization NeurIPS 2024