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stochastic gradient descent

1088 papers

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Also known as

SGD

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

SGD for Structured Nonconvex Functions: Learning Rates, Minibatching and Interpolation AISTATS 2021
Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiency AISTATS 2021
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms NIPS 2021
Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD NIPS 2021
On the Convergence of Step Decay Step-Size for Stochastic Optimization NIPS 2021
On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs) NIPS 2021
L-SVRG and L-Katyusha with Arbitrary Sampling JMLR 2021
Domain-Independent Dominance of Adaptive Methods CVPR 2021
Online stochastic gradient descent on non-convex losses from high-dimensional inference JMLR 2021
Neural Networks Learning and Memorization with (almost) no Over-Parameterization NIPS 2020
Optimizing Millions of Hyperparameters by Implicit Differentiation AISTATS 2020
A Rule for Gradient Estimator Selection, with an Application to Variational Inference AISTATS 2020
On the Convergence of SARAH and Beyond AISTATS 2020
Choosing the Sample with Lowest Loss makes SGD Robust AISTATS 2020
FNNC: Achieving Fairness through Neural Networks IJCAI 2020
Accelerating Stratified Sampling SGD by Reconstructing Strata IJCAI 2020
Stochastic Nested Variance Reduction for Nonconvex Optimization JMLR 2020
A Unified Framework of Online Learning Algorithms for Training Recurrent Neural Networks JMLR 2020
Convergence Rates for the Stochastic Gradient Descent Method for Non-Convex Objective Functions JMLR 2020
Asymptotic Analysis via Stochastic Differential Equations of Gradient Descent Algorithms in Statistical and Computational Paradigms JMLR 2020
Theory of Curriculum Learning, with Convex Loss Functions JMLR 2020
The Error-Feedback framework: SGD with Delayed Gradients JMLR 2020
Bounding the expected run-time of nonconvex optimization with early stopping UAI 2020
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC ICML 2020
FetchSGD: Communication-Efficient Federated Learning with Sketching ICML 2020