Stochastic Methods

Online Learning: Stochastic, Constrained, and Smoothed Adversaries NeurIPS 2011 Quasi-Newton Methods for Markov Chain Monte Carlo NeurIPS 2011 Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC NeurIPS 2011 Stochastic convex optimization with bandit feedback NeurIPS 2011 Stochastic Methods for -regularized Loss Minimization JMLR 2011 The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond COLT 2011 A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping AISTATS 2010 Approximate inference in continuous time Gaussian-Jump processes NeurIPS 2010 A rational decision making framework for inhibitory control NeurIPS 2010 Dual Averaging Methods for Regularized Stochastic Learning and Online Optimization JMLR 2010 Elliptical slice sampling AISTATS 2010 Exploiting Feature Covariance in High-Dimensional Online Learning AISTATS 2010 Fast and Scalable Local Kernel Machines JMLR 2010 Gaussian sampling by local perturbations NeurIPS 2010 HOP-MAP: Efficient Message Passing with High Order Potentials AISTATS 2010 Improving the Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices NeurIPS 2010 Inference and Learning in Networks of Queues AISTATS 2010 Learning with Blocks: Composite Likelihood and Contrastive Divergence AISTATS 2010 Multi-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition NeurIPS 2010 Network Flow Algorithms for Structured Sparsity NeurIPS 2010 Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable NeurIPS 2010 Online Learning for Matrix Factorization and Sparse Coding JMLR 2010 Parallelizable Sampling of Markov Random Fields AISTATS 2010 Parallelized Stochastic Gradient Descent NeurIPS 2010 Random Walk Approach to Regret Minimization NeurIPS 2010