Stochastic Methods

Accelerated Gradient Methods for Stochastic Optimization and Online Learning NeurIPS 2009 Dual Averaging Method for Regularized Stochastic Learning and Online Optimization NeurIPS 2009 Learning in Markov Random Fields using Tempered Transitions NeurIPS 2009 Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling NeurIPS 2009 Online Learning of Assignments NeurIPS 2009 Randomized Pruning: Efficiently Calculating Expectations in Large Dynamic Programs NeurIPS 2009 Slow Learners are Fast NeurIPS 2009 Adapting to a Market Shock: Optimal Sequential Market-Making NeurIPS 2008 Algorithms for Infinitely Many-Armed Bandits NeurIPS 2008 An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering NeurIPS 2008 An interior-point stochastic approximation method and an L1-regularized delta rule NeurIPS 2008 Evaluating probabilities under high-dimensional latent variable models NeurIPS 2008 Fast Rates for Regularized Objectives NeurIPS 2008 Mortal Multi-Armed Bandits NeurIPS 2008 One sketch for all: Theory and Application of Conditional Random Sampling NeurIPS 2008 Particle Filter-based Policy Gradient in POMDPs NeurIPS 2008 Regularized Policy Iteration NeurIPS 2008 Signal-to-Noise Ratio Analysis of Policy Gradient Algorithms NeurIPS 2008 Stochastic Relational Models for Large-scale Dyadic Data using MCMC NeurIPS 2008 A Randomized Algorithm for Large Scale Support Vector Learning NeurIPS 2007 Loop Corrections for Approximate Inference on Factor Graphs JMLR 2007 Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods NeurIPS 2007 Scan Strategies for Meteorological Radars NeurIPS 2007 Simulated Annealing: Rigorous finite-time guarantees for optimization on continuous domains NeurIPS 2007 Temporal Difference Updating without a Learning Rate NeurIPS 2007