stochastic gradient descent

Momentum-Based Variance Reduction in Non-Convex SGD NeurIPS 2019 New Convergence Aspects of Stochastic Gradient Algorithms JMLR 2019 Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems NeurIPS 2019 Normal Approximation for Stochastic Gradient Descent via Non-Asymptotic Rates of Martingale CLT COLT 2019 Nostalgic Adam: Weighting More of the Past Gradients When Designing the Adaptive Learning Rate IJCAI 2019 On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization ICML 2019 On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes AISTATS 2019 On the Convergence of (Stochastic) Gradient Descent with Extrapolation for Non-Convex Minimization IJCAI 2019 On the Convergence Rate of Training Recurrent Neural Networks NeurIPS 2019 On the Ineffectiveness of Variance Reduced Optimization for Deep Learning NeurIPS 2019 On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization ICML 2019 On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length ICLR 2019 Open Problem: Do Good Algorithms Necessarily Query Bad Points? COLT 2019 P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification CVPR 2019 Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates NeurIPS 2019 Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning AAAI 2019 Parsimonious Online Learning with Kernels via Sparse Projections in Function Space JMLR 2019 Projection Free Online Learning over Smooth Sets AISTATS 2019 Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations NeurIPS 2019 Quasi-hyperbolic momentum and Adam for deep learning ICLR 2019 Random Shuffling Beats SGD after Finite Epochs ICML 2019 Renyi Differentially Private ERM for Smooth Objectives AISTATS 2019 REPAIR: Removing Representation Bias by Dataset Resampling CVPR 2019 Riemannian Adaptive Optimization Methods ICLR 2019 RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets AAAI 2019