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

The Implicit Bias of Heterogeneity towards Invariance: A Study of Multi-Environment Matrix Sensing NIPS 2024

Generalization Bounds for Label Noise Stochastic Gradient Descent AISTATS 2024

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

High Probability Convergence Bounds for Non-convex Stochastic Gradient Descent with Sub-Weibull Noise JMLR 2024

Revisiting the Noise Model of Stochastic Gradient Descent AISTATS 2024

Law of Large Numbers and Central Limit Theorem for Wide Two-layer Neural Networks: The Mini-Batch and Noisy Case JMLR 2024

Symmetries in Overparametrized Neural Networks: A Mean Field View NIPS 2024

Continual learning with the neural tangent ensemble NIPS 2024

Iterate Averaging in the Quest for Best Test Error JMLR 2024

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

The Road Less Scheduled NIPS 2024

Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning JMLR 2024

Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? AISTATS 2024

Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent JMLR 2024

Improved Sample Complexity Analysis of Natural Policy Gradient Algorithm with General Parameterization for Infinite Horizon Discounted Reward Markov Decision Processes AISTATS 2024

A General Algorithm for Solving Rank-one Matrix Sensing AISTATS 2024

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

On the Hyperparameters in Stochastic Gradient Descent with Momentum JMLR 2024

A large deviations perspective on policy gradient algorithms L4DC 2024

DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction NIPS 2024

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

A Comparison of Continuous-Time Approximations to Stochastic Gradient Descent JMLR 2024

GGD: Grafting Gradient Descent JMLR 2024

Parameter Symmetry and Noise Equilibrium of Stochastic Gradient Descent NIPS 2024

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