Variational Inference

Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning NeurIPS 2015 A Bayesian model for identifying hierarchically organised states in neural population activity NeurIPS 2014 A Complete Variational Tracker NeurIPS 2014 A Filtering Approach to Stochastic Variational Inference NeurIPS 2014 Analysis of Empirical MAP and Empirical Partially Bayes: Can They be Alternatives to Variational Bayes? AISTATS 2014 Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP NeurIPS 2014 Approximate Slice Sampling for Bayesian Posterior Inference AISTATS 2014 Automated Variational Inference for Gaussian Process Models NeurIPS 2014 Black Box Variational Inference AISTATS 2014 Clustered factor analysis of multineuronal spike data NeurIPS 2014 Concavity of reweighted Kikuchi approximation NeurIPS 2014 Content-based recommendations with Poisson factorization NeurIPS 2014 Decoupled Variational Gaussian Inference NeurIPS 2014 Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models NeurIPS 2014 Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights NeurIPS 2014 Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables AISTATS 2014 Extended and Unscented Gaussian Processes NeurIPS 2014 Fast Allocation of Gaussian Process Experts ICML 2014 From MAP to Marginals: Variational Inference in Bayesian Submodular Models NeurIPS 2014 Improved Multimodal Deep Learning with Variation of Information NeurIPS 2014 On Prior Distributions and Approximate Inference for Structured Variables NeurIPS 2014 PAC-Bayesian AUC classification and scoring NeurIPS 2014 Robust Principal Component Analysis with Complex Noise ICML 2014 Scalable Variational Bayesian Matrix Factorization with Side Information AISTATS 2014 Semi-supervised Learning with Deep Generative Models NeurIPS 2014