Variational Inference

Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes JMLR 2010 Global Analytic Solution for Variational Bayesian Matrix Factorization NeurIPS 2010 Group Sparse Coding with a Laplacian Scale Mixture Prior NeurIPS 2010 Improving posterior marginal approximations in latent Gaussian models AISTATS 2010 Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development NeurIPS 2010 Nonparametric Bayesian Matrix Factorization by Power-EP AISTATS 2010 Variational bounds for mixed-data factor analysis NeurIPS 2010 Variational Inference over Combinatorial Spaces NeurIPS 2010 A Gaussian Tree Approximation for Integer Least-Squares NeurIPS 2009 A Stochastic approximation method for inference in probabilistic graphical models NeurIPS 2009 Learning a Small Mixture of Trees NeurIPS 2009 Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units NeurIPS 2009 Particle-based Variational Inference for Continuous Systems NeurIPS 2009 Posterior vs Parameter Sparsity in Latent Variable Models NeurIPS 2009 Variational Gaussian-process factor analysis for modeling spatio-temporal data NeurIPS 2009 Variational Inference for the Nested Chinese Restaurant Process NeurIPS 2009 Evaluating probabilities under high-dimensional latent variable models NeurIPS 2008 Gates NeurIPS 2008 Sparse probabilistic projections NeurIPS 2008 Variational Mixture of Gaussian Process Experts NeurIPS 2008 Building Blocks for Variational Bayesian Learning of Latent Variable Models JMLR 2007 Collapsed Variational Inference for HDP NeurIPS 2007 Loop Series and Bethe Variational Bounds in Attractive Graphical Models NeurIPS 2007 Neural characterization in partially observed populations of spiking neurons NeurIPS 2007 The Infinite Gamma-Poisson Feature Model NeurIPS 2007