stochastic approximation

Scalable Gaussian Process Classification via Expectation Propagation AISTATS 2016 Stochastic Discrete Clenshaw-Curtis Quadrature ICML 2016 Weak Convergence Properties of Constrained Emphatic Temporal-difference Learning with Constant and Slowly Diminishing Stepsize JMLR 2016 Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care NeurIPS 2015 Large-scale log-determinant computation through stochastic Chebyshev expansions ICML 2015 SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk NeurIPS 2015 The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling ICML 2015 Towards Maximum Likelihood: Learning Undirected Graphical Models using Persistent Sequential Monte Carlo ACML 2014 Universal Option Models NeurIPS 2014 Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n) NeurIPS 2013 Adaptive Metropolis with Online Relabeling AISTATS 2012 Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning NeurIPS 2011 Approximation of hidden Markov models by mixtures of experts with application to particle filtering AISTATS 2010 Multi-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition NeurIPS 2010 Online Learning for Matrix Factorization and Sparse Coding JMLR 2010 A Stochastic approximation method for inference in probabilistic graphical models NeurIPS 2009 Learning in Markov Random Fields using Tempered Transitions NeurIPS 2009 An interior-point stochastic approximation method and an L1-regularized delta rule NeurIPS 2008 A Simulation-Based Algorithm for Ergodic Control of Markov Chains Conditioned on Rare Events JMLR 2006