stochastic gradient descent

Differentially Private Coordinate Descent for Composite Empirical Risk Minimization ICML 2022 Differentially Private Learning Needs Hidden State (Or Much Faster Convergence) NeurIPS 2022 Differentially private SGDA for minimax problems UAI 2022 Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Convergence to Local Minima JMLR 2022 Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution NeurIPS 2022 Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks NeurIPS 2022 Efficiency Ordering of Stochastic Gradient Descent NeurIPS 2022 Efficient Decentralized Stochastic Gradient Descent Method for Nonconvex Finite-Sum Optimization Problems AAAI 2022 Efficient Sharpness-aware Minimization for Improved Training of Neural Networks ICLR 2022 Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums ICLR 2022 Eliminating Sharp Minima from SGD with Truncated Heavy-tailed Noise ICLR 2022 Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning NeurIPS 2022 Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions With Superior OOD Generalization CVPR 2022 Exploiting Explainable Metrics for Augmented SGD CVPR 2022 Fast and Robust Online Inference with Stochastic Gradient Descent via Random Scaling AAAI 2022 Faster Privacy Accounting via Evolving Discretization ICML 2022 Fast Mixing of Stochastic Gradient Descent with Normalization and Weight Decay NeurIPS 2022 FastSHAP: Real-Time Shapley Value Estimation ICLR 2022 Federated Minimax Optimization: Improved Convergence Analyses and Algorithms ICML 2022 Fine-tuning Language Models over Slow Networks using Activation Quantization with Guarantees NeurIPS 2022 Fourier Learning with Cyclical Data ICML 2022 Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits ICLR 2022 From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent NeurIPS 2022 Generalization Bounds for Gradient Methods via Discrete and Continuous Prior NeurIPS 2022 Generalization Bounds for Stochastic Gradient Descent via Localized ε-Covers NeurIPS 2022