stochastic gradient

Stochastic Gradient Monomial Gamma Sampler ICML 2017 Variational Inference for Gaussian Process Models with Linear Complexity NeurIPS 2017 Accelerating Stochastic Composition Optimization NeurIPS 2016 A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order NeurIPS 2016 A Multi-Batch L-BFGS Method for Machine Learning NeurIPS 2016 Bayesian Optimization with Robust Bayesian Neural Networks NeurIPS 2016 Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization AISTATS 2016 Dimension-Free Iteration Complexity of Finite Sum Optimization Problems NeurIPS 2016 DSA: Decentralized Double Stochastic Averaging Gradient Algorithm JMLR 2016 Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters NeurIPS 2016 Matrix Eigen-decomposition via Doubly Stochastic Riemannian Optimization ICML 2016 On Graduated Optimization for Stochastic Non-Convex Problems ICML 2016 Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization NeurIPS 2016 Robust Monte Carlo Sampling using Riemannian Nosé-Poincaré Hamiltonian Dynamics ICML 2016 Scalable Adaptive Stochastic Optimization Using Random Projections NeurIPS 2016 Scalable MCMC for Mixed Membership Stochastic Blockmodels AISTATS 2016 Stochastic Gradient Geodesic MCMC Methods NeurIPS 2016 Stochastic Gradient MCMC with Stale Gradients NeurIPS 2016 Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences NeurIPS 2016 Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo NeurIPS 2016 Stochastic Quasi-Newton Langevin Monte Carlo ICML 2016 Stochastic Three-Composite Convex Minimization NeurIPS 2016 Stochastic Variational Deep Kernel Learning NeurIPS 2016 The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM NeurIPS 2016 Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient ICML 2016