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stochastic gradient descent

1088 papers

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Also known as

SGD ASGD SAGA SGM SGDA PSGD SKGD

Co-occurring keywords

non-convex optimization (546) distributed learning (563) neural network optimization (1293) convergence rate (606) convex optimization (1320) neural network (6616) variance reduction (520) stochastic optimization (1060) convergence analysis (394) differential privacy (1010)

Papers

Fast Mixing of Stochastic Gradient Descent with Normalization and Weight Decay NIPS 2022
Generalization Bounds for Gradient Methods via Discrete and Continuous Prior NIPS 2022
Better SGD using Second-order Momentum NIPS 2022
Generalization Bounds for Stochastic Gradient Descent via Localized $\varepsilon$-Covers NIPS 2022
Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams NIPS 2022
Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability CVPR 2022
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence) NIPS 2022
A Statistical Online Inference Approach in Averaged Stochastic Approximation NIPS 2022
Momentum Aggregation for Private Non-convex ERM NIPS 2022
Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape AISTATS 2022
Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks NIPS 2022
Variance Reduced EXTRA and DIGing and Their Optimal Acceleration for Strongly Convex Decentralized Optimization JMLR 2022
Towards Efficient and Scalable Sharpness-Aware Minimization CVPR 2022
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions With Superior OOD Generalization CVPR 2022
On the Theoretical Properties of Noise Correlation in Stochastic Optimization NIPS 2022
Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions NIPS 2022
Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise NIPS 2022
Multi-agent Performative Prediction with Greedy Deployment and Consensus Seeking Agents NIPS 2022
Understanding Stochastic Optimization Behavior at the Layer Update Level (Student Abstract) AAAI 2022
Riemannian Stochastic Proximal Gradient Methods for Nonsmooth Optimization over the Stiefel Manifold JMLR 2022
Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions NIPS 2022
Fourier Learning with Cyclical Data ICML 2022
Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss NIPS 2022
Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods COLT 2022
Masked Training of Neural Networks with Partial Gradients AISTATS 2022