non-convex optimization

Nesterov acceleration in benignly non-convex landscapes ICLR 2025 Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning JMLR 2025 Nonconvex Stochastic Optimization under Heavy-Tailed Noises: Optimal Convergence without Gradient Clipping ICLR 2025 Non-Convex Tensor Recovery from Local Measurements AAAI 2025 Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods ICLR 2025 Problem-Parameter-Free Federated Learning ICLR 2025 Revisiting Gradient Normalization and Clipping for Nonconvex SGD under Heavy-Tailed Noise: Necessity, Sufficiency, and Acceleration JMLR 2025 Revisiting Large-Scale Non-convex Distributionally Robust Optimization ICLR 2025 Robust Gradient Descent for Phase Retrieval AISTATS 2025 Sharp Bounds for Sequential Federated Learning on Heterogeneous Data JMLR 2025 Sharpness-Aware Minimization: General Analysis and Improved Rates ICLR 2025 StableMDS: A Novel Gradient Descent-Based Method for Stabilizing and Accelerating Weighted Multidimensional Scaling AISTATS 2025 Two-Timescale Gradient Descent Ascent Algorithms for Nonconvex Minimax Optimization JMLR 2025 Understanding the Stability-based Generalization of Personalized Federated Learning ICLR 2025 Vertical Federated Learning with Missing Features During Training and Inference ICLR 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 Absence of spurious solutions far from ground truth: A low-rank analysis with high-order losses AISTATS 2024 Acceleration Exists! Optimization Problems When Oracle Can Only Compare Objective Function Values NeurIPS 2024 Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes NeurIPS 2024 Adam with model exponential moving average is effective for nonconvex optimization NeurIPS 2024 Adaptive Federated Minimax Optimization with Lower Complexities AISTATS 2024 Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions NeurIPS 2024 A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs NeurIPS 2024 A large deviations perspective on policy gradient algorithms L4DC 2024