Variational Inference

High-dimensional neural spike train analysis with generalized count linear dynamical systems NeurIPS 2015 Inference for determinantal point processes without spectral knowledge NeurIPS 2015 Kullback-Leibler Proximal Variational Inference NeurIPS 2015 Learning Deep Sigmoid Belief Networks with Data Augmentation AISTATS 2015 Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels NeurIPS 2015 Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes NeurIPS 2015 Local Expectation Gradients for Black Box Variational Inference NeurIPS 2015 Low-Rank Matrix Factorization Under General Mixture Noise Distributions ICCV 2015 MCMC for Variationally Sparse Gaussian Processes NeurIPS 2015 Minimum Weight Perfect Matching via Blossom Belief Propagation NeurIPS 2015 Neural Adaptive Sequential Monte Carlo NeurIPS 2015 On some provably correct cases of variational inference for topic models NeurIPS 2015 Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference NeurIPS 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 Inference for Gaussian Process Models with Black-Box Likelihoods NeurIPS 2015 Scalable Variational Gaussian Process Classification AISTATS 2015 Stochastic Expectation Propagation NeurIPS 2015 Stochastic Structured Variational Inference AISTATS 2015 Streaming, Distributed Variational Inference for Bayesian Nonparametrics NeurIPS 2015 The Population Posterior and Bayesian Modeling on Streams NeurIPS 2015 Variational Consensus Monte Carlo NeurIPS 2015 Variational Dropout and the Local Reparameterization Trick NeurIPS 2015