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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

Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence NIPS 2019
Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD NIPS 2019
Using Statistics to Automate Stochastic Optimization NIPS 2019
Efficient Convex Relaxations for Streaming PCA NIPS 2019
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models NIPS 2019
Learning with Non-Convex Truncated Losses by SGD UAI 2019
Subspace Inference for Bayesian Deep Learning UAI 2019
Faster Distributed Deep Net Training: Computation and Communication Decoupled Stochastic Gradient Descent IJCAI 2019
Semi-Cyclic Stochastic Gradient Descent ICML 2019
A Universally Optimal Multistage Accelerated Stochastic Gradient Method NIPS 2019
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations NIPS 2019
Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems NIPS 2019
Efficient Diversified Mini-Batch Selection using Variable High-layer Features ACML 2019
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data AISTATS 2019
Projection Free Online Learning over Smooth Sets AISTATS 2019
Random Shuffling Beats SGD after Finite Epochs ICML 2019
Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization AISTATS 2019
DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression ICML 2019
Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data ICML 2019
New Convergence Aspects of Stochastic Gradient Algorithms JMLR 2019
A Latent Variational Framework for Stochastic Optimization NIPS 2019
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates NIPS 2019
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares NIPS 2019
User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning NIPS 2019
Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD ICML 2019