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

Coupling-based Convergence Diagnostic and Stepsize Scheme for Stochastic Gradient Descent AAAI 2025

Optimal Decentralized Composite Optimization for Strongly Convex Functions JMLR 2025

Improving Deep Learning Speed and Performance Through Synaptic Neural Balance AAAI 2025

Error Analysis Affected by Heavy-Tailed Gradients for Non-Convex Pairwise Stochastic Gradient Descent AAAI 2025

Estimating Generalization Performance Along the Trajectory of Proximal SGD in Robust Regression NIPS 2024

Bias in Motion: Theoretical Insights into the Dynamics of Bias in SGD Training NIPS 2024

AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms AISTATS 2024

The Implicit Bias of Adam on Separable Data NIPS 2024

Banded Square Root Matrix Factorization for Differentially Private Model Training NIPS 2024

Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters AISTATS 2024

Near-Optimal Streaming Heavy-Tailed Statistical Estimation with Clipped SGD NIPS 2024

Stochastic Optimization Schemes for Performative Prediction with Nonconvex Loss NIPS 2024

Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient Methods AISTATS 2024

Diagonalisation SGD: Fast & Convergent SGD for Non-Differentiable Models via Reparameterisation and Smoothing AISTATS 2024

Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training AISTATS 2024

Learn To Unlearn for Deep Neural Networks: Minimizing Unlearning Interference With Gradient Projection WACV 2024

Revisiting the Noise Model of Stochastic Gradient Descent AISTATS 2024

Tight Convergence Rate Bounds for Optimization Under Power Law Spectral Conditions JMLR 2024

Almost Sure Convergence Rates Analysis and Saddle Avoidance of Stochastic Gradient Methods JMLR 2024

Small steps no more: Global convergence of stochastic gradient bandits for arbitrary learning rates NIPS 2024

Why is parameter averaging beneficial in SGD? An objective smoothing perspective AISTATS 2024

Overparametrized Multi-layer Neural Networks: Uniform Concentration of Neural Tangent Kernel and Convergence of Stochastic Gradient Descent JMLR 2024

Scalable DP-SGD: Shuffling vs. Poisson Subsampling NIPS 2024

Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis NIPS 2024

Continual learning with the neural tangent ensemble NIPS 2024