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stochastic gradient

296 papers

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Also known as

SGVB SVRG SGHMC SGLD SG SGMCMC SGD

Co-occurring keywords

variance reduction (520) markov chain monte carlo (724) stochastic optimization (1060) bayesian inference (1904) stochastic gradient descent (1088) convex optimization (1320) nonconvex optimization (316) convergence rate (606) langevin dynamics (128) variational inference (2069)

Papers

ATOMO: Communication-efficient Learning via Atomic Sparsification NIPS 2018
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs ICML 2018
Gradient Sparsification for Communication-Efficient Distributed Optimization NIPS 2018
Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates NIPS 2018
Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization NIPS 2018
Learning To Learn Around A Common Mean NIPS 2018
Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation NIPS 2018
Stochastic Cubic Regularization for Fast Nonconvex Optimization NIPS 2018
Efficient Stochastic Gradient Hard Thresholding NIPS 2018
Quasi-Monte Carlo Variational Inference ICML 2018
Gaussian Process Prior Variational Autoencoders NIPS 2018
Total stochastic gradient algorithms and applications in reinforcement learning NIPS 2018
Riemannian Stochastic Recursive Gradient Algorithm ICML 2018
Online Learning with Non-Convex Losses and Non-Stationary Regret AISTATS 2018
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity ICML 2018
Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters NIPS 2018
Global Non-convex Optimization with Discretized Diffusions NIPS 2018
Large-Scale Stochastic Sampling from the Probability Simplex NIPS 2018
A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization NIPS 2018
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning NIPS 2018
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo ICML 2018
Tracking the gradients using the Hessian: A new look at variance reducing stochastic methods AISTATS 2018
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients ICML 2018
Sharp Minima Can Generalize For Deep Nets ICML 2017
Natasha: Faster Non-Convex Stochastic Optimization via Strongly Non-Convex Parameter ICML 2017