variational inference

Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes NeurIPS 2015 Local Expectation Gradients for Black Box Variational Inference NeurIPS 2015 Markov Chain Monte Carlo and Variational Inference: Bridging the Gap ICML 2015 MCMC for Variationally Sparse Gaussian Processes NeurIPS 2015 Nested Sequential Monte Carlo Methods ICML 2015 Neural Adaptive Sequential Monte Carlo NeurIPS 2015 Non-Gaussian Discriminative Factor Models via the Max-Margin Rank-Likelihood ICML 2015 On some provably correct cases of variational inference for topic models NeurIPS 2015 Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks ICML 2015 Probabilistic Variational Bounds for Graphical Models NeurIPS 2015 Regularized EM Algorithms: A Unified Framework and Statistical Guarantees NeurIPS 2015 Reliable and Scalable Variational Inference for the Hierarchical Dirichlet Process AISTATS 2015 Reweighted Laplace Prior Based Hyperspectral Compressive Sensing for Unknown Sparsity CVPR 2015 Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models NeurIPS 2015 Scalable Deep Poisson Factor Analysis for Topic Modeling ICML 2015 Scalable Inference for Gaussian Process Models with Black-Box Likelihoods NeurIPS 2015 Scalable Nonparametric Multiway Data Analysis AISTATS 2015 Scalable Variational Gaussian Process Classification AISTATS 2015 Scalable Variational Inference in Log-supermodular Models ICML 2015 Sensor Selection for Crowdsensing Dynamical Systems AISTATS 2015 Sparse Variational Inference for Generalized GP Models ICML 2015 Stochastic Expectation Propagation NeurIPS 2015 Streaming, Distributed Variational Inference for Bayesian Nonparametrics NeurIPS 2015 Streaming Variational Inference for Dirichlet Process Mixtures ACML 2015 The Benefits of Learning with Strongly Convex Approximate Inference ICML 2015