← Optimization & Theory
Machine Learning › Optimization & Theory ›

Stochastic Methods

1077 directly classified papers

Papers per year

Papers

Memory (and Time) Efficient Sequential Monte Carlo ICML 2014
Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach ICML 2014
Efficiently Sampling Probabilistic Programs via Program Analysis AISTATS 2013
Annealing between distributions by averaging moments NIPS 2013
Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent NIPS 2013
Fast Determinantal Point Process Sampling with Application to Clustering NIPS 2013
Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization NIPS 2013
Stochastic Optimization of PCA with Capped MSG NIPS 2013
Embed and Project: Discrete Sampling with Universal Hashing NIPS 2013
Near-Optimal Entrywise Sampling for Data Matrices NIPS 2013
Variance Reduction for Stochastic Gradient Optimization NIPS 2013
Thompson Sampling for 1-Dimensional Exponential Family Bandits NIPS 2013
Blind Calibration in Compressed Sensing using Message Passing Algorithms NIPS 2013
Accelerating Stochastic Gradient Descent using Predictive Variance Reduction NIPS 2013
Learning CRFs for Image Parsing with Adaptive Subgradient Descent ICCV 2013
Distributed and Adaptive Darting Monte Carlo through Regenerations AISTATS 2013
BudgetedSVM: A Toolbox for Scalable SVM Approximations JMLR 2013
No more pesky learning rates ICML 2013
Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models ICML 2013
Approximate Inference in Collective Graphical Models ICML 2013
Stable Coactive Learning via Perturbation ICML 2013
Loss-Proportional Subsampling for Subsequent ERM ICML 2013
Large-Scale Learning with Less RAM via Randomization ICML 2013
A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel Learning ICML 2013
Dual Averaging and Proximal Gradient Descent for Online Alternating Direction Multiplier Method ICML 2013