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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

Online Partial Least Square Optimization: Dropping Convexity for Better Efficiency and Scalability ICML 2017
On Structured Prediction Theory with Calibrated Convex Surrogate Losses NIPS 2017
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent NIPS 2017
Multiscale Quantization for Fast Similarity Search NIPS 2017
Deep Hyperalignment NIPS 2017
Training Deep Networks without Learning Rates Through Coin Betting NIPS 2017
The Marginal Value of Adaptive Gradient Methods in Machine Learning NIPS 2017
Differentially Private Empirical Risk Minimization Revisited: Faster and More General NIPS 2017
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization NIPS 2017
Safe Adaptive Importance Sampling NIPS 2017
Online to Offline Conversions, Universality and Adaptive Minibatch Sizes NIPS 2017
Fast Parallel Training of Neural Language Models IJCAI 2017
SGD Learns the Conjugate Kernel Class of the Network NIPS 2017
Stabilized Sparse Online Learning for Sparse Data JMLR 2017
A Unified Variance Reduction-Based Framework for Nonconvex Low-Rank Matrix Recovery ICML 2017
Relativistic Monte Carlo AISTATS 2017
Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation CVPR 2017
The Scaling Limit of High-Dimensional Online Independent Component Analysis NIPS 2017
Optimal Rates for Multi-pass Stochastic Gradient Methods JMLR 2017
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression JMLR 2017
Empirical Risk Minimization for Stochastic Convex Optimization: $O(1/n)$- and $O(1/n^2)$-type of Risk Bounds COLT 2017
Stochastic Gradient MCMC Methods for Hidden Markov Models ICML 2017
Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox COLT 2017
Tux²: Distributed Graph Computation for Machine Learning NSDI 2017
Fast Residual Forests: Rapid Ensemble Learning for Semantic Segmentation CORL 2017