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Stochastic Methods

97 directly classified papers

Papers per year

Papers

SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator NIPS 2018
A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication NIPS 2018
Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks NIPS 2018
Reducing Reparameterization Gradient Variance NIPS 2017
Differentiable Scheduled Sampling for Credit Assignment ACL 2017
Training Deep Networks without Learning Rates Through Coin Betting NIPS 2017
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks NIPS 2017
Using Spatial Order to Boost the Elimination of Incorrect Feature Matches CVPR 2016
Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters NIPS 2016
Distributed Flexible Nonlinear Tensor Factorization NIPS 2016
Random Features for Sparse Signal Classification CVPR 2016
StopWasting My Gradients: Practical SVRG NIPS 2015
A Complete Recipe for Stochastic Gradient MCMC NIPS 2015
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms NIPS 2015
Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients NIPS 2015
Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization NIPS 2015
Variance Reduced Stochastic Gradient Descent with Neighbors NIPS 2015
Stochastic Proximal Gradient Descent with Acceleration Techniques NIPS 2014
Variance Reduction for Stochastic Gradient Optimization NIPS 2013
Stochastic Optimization of PCA with Capped MSG NIPS 2013
LSTD with Random Projections NIPS 2010
Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains NIPS 2007