variance reduction

PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization JMLR 2024 Quantum Algorithms for Non-smooth Non-convex Optimization NeurIPS 2024 ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models ICML 2024 Robust Fine-tuning of Zero-shot Models via Variance Reduction NeurIPS 2024 Sample Complexity of Variance-Reduced Distributionally Robust Q-Learning JMLR 2024 SILVER: Single-loop variance reduction and application to federated learning ICML 2024 Sketchy Moment Matching: Toward Fast and Provable Data Selection for Finetuning NeurIPS 2024 Sliced Wasserstein Estimation with Control Variates ICLR 2024 Stochastic Frank-Wolfe: Unified Analysis and Zoo of Special Cases AISTATS 2024 The Benefits of Balance: From Information Projections to Variance Reduction NeurIPS 2024 Truncated Variance Reduced Value Iteration NeurIPS 2024 Trust Region Methods for Nonconvex Stochastic Optimization beyond Lipschitz Smoothness AAAI 2024 UNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecution AAAI 2024 Universality of AdaGrad Stepsizes for Stochastic Optimization: Inexact Oracle, Acceleration and Variance Reduction NeurIPS 2024 Variance Reduced Halpern Iteration for Finite-Sum Monotone Inclusions ICLR 2024 Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML NeurIPS 2024 Accelerated Cyclic Coordinate Dual Averaging with Extrapolation for Composite Convex Optimization ICML 2023 A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting NeurIPS 2023 AdaGDA: Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization AISTATS 2023 Adaptive SGD with Polyak stepsize and Line-search: Robust Convergence and Variance Reduction NeurIPS 2023 A Long N-step Surrogate Stage Reward for Deep Reinforcement Learning NeurIPS 2023 A Variance-Reduced and Stabilized Proximal Stochastic Gradient Method with Support Identification Guarantees for Structured Optimization AISTATS 2023 Beyond ADMM: A Unified Client-Variance-Reduced Adaptive Federated Learning Framework AAAI 2023 Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization ICML 2023 Breaking the Lower Bound with (Little) Structure: Acceleration in Non-Convex Stochastic Optimization with Heavy-Tailed Noise COLT 2023