variational inference

Theoretical Analysis of Bayesian Matrix Factorization JMLR 2011 Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL Contraction NeurIPS 2011 Variational Gaussian Process Dynamical Systems NeurIPS 2011 Variational Learning for Recurrent Spiking Networks NeurIPS 2011 Approximate Inference by Compilation to Arithmetic Circuits NeurIPS 2010 Approximate inference in continuous time Gaussian-Jump processes NeurIPS 2010 Bayesian Gaussian Process Latent Variable Model AISTATS 2010 Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing JMLR 2010 Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations NeurIPS 2010 Cross Species Expression Analysis using a Dirichlet Process Mixture Model with Latent Matchings NeurIPS 2010 Dense Message Passing for Sparse Principal Component Analysis AISTATS 2010 Efficient Multioutput Gaussian Processes through Variational Inducing Kernels AISTATS 2010 FastInf: An Efficient Approximate Inference Library JMLR 2010 Fast Large-scale Mixture Modeling with Component-specific Data Partitions NeurIPS 2010 Gaussian Processes for Machine Learning (GPML) Toolbox JMLR 2010 Gaussian sampling by local perturbations NeurIPS 2010 Group Sparse Coding with a Laplacian Scale Mixture Prior NeurIPS 2010 Implicit Differentiation by Perturbation NeurIPS 2010 Improving posterior marginal approximations in latent Gaussian models AISTATS 2010 Incremental Sigmoid Belief Networks for Grammar Learning JMLR 2010 Large Margin Learning of Upstream Scene Understanding Models NeurIPS 2010 Mean Field Variational Approximation for Continuous-Time Bayesian Networks JMLR 2010 Nonparametric Bayesian Matrix Factorization by Power-EP AISTATS 2010 Online Learning for Latent Dirichlet Allocation NeurIPS 2010 Simple Exponential Family PCA AISTATS 2010