stochastic gradient descent

LIBOL: A Library for Online Learning Algorithms JMLR 2014 Online and Stochastic Gradient Methods for Non-decomposable Loss Functions NeurIPS 2014 On the Computational Efficiency of Training Neural Networks NeurIPS 2014 Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning NeurIPS 2014 Statistical analysis of stochastic gradient methods for generalized linear models ICML 2014 Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm NeurIPS 2014 Stochastic Proximal Gradient Descent with Acceleration Techniques NeurIPS 2014 Training Highly Multiclass Classifiers JMLR 2014 Accelerating Stochastic Gradient Descent using Predictive Variance Reduction NeurIPS 2013 Adaptive dropout for training deep neural networks NeurIPS 2013 BudgetedSVM: A Toolbox for Scalable SVM Approximations JMLR 2013 Communication-Efficient Algorithms for Statistical Optimization JMLR 2013 Efficient Large-Scale Structured Learning CVPR 2013 Fast Probabilistic Optimization from Noisy Gradients ICML 2013 Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions NeurIPS 2013 Linear Convergence with Condition Number Independent Access of Full Gradients NeurIPS 2013 More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server NeurIPS 2013 No more pesky learning rates ICML 2013 Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) NeurIPS 2013 On the importance of initialization and momentum in deep learning ICML 2013 Second Order Online Collaborative Filtering ACML 2013 Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization JMLR 2013 Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes ICML 2013 Stochastic Optimization of PCA with Capped MSG NeurIPS 2013 The Fast Convergence of Incremental PCA NeurIPS 2013