Bayesian Inference

Scalable Gaussian Process Structured Prediction for Grid Factor Graph Applications ICML 2014 Scalable Inference for Neuronal Connectivity from Calcium Imaging NeurIPS 2014 Scalable Variational Bayesian Matrix Factorization with Side Information AISTATS 2014 Scaling Nonparametric Bayesian Inference via Subsample-Annealing AISTATS 2014 Scaling-up Importance Sampling for Markov Logic Networks NeurIPS 2014 Semantic Localization Via the Matrix Permanent RSS 2014 Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models NeurIPS 2014 Semi-supervised Learning with Deep Generative Models NeurIPS 2014 Sequential crowdsourced labeling as an epsilon-greedy exploration in a Markov Decision Process AISTATS 2014 Sequential Monte Carlo for Graphical Models NeurIPS 2014 Smoothed Gradients for Stochastic Variational Inference NeurIPS 2014 Sparse Bayesian structure learning with “dependent relevance determination” priors NeurIPS 2014 Sparse Bayesian Variable Selection for the Identification of Antigenic Variability in the Foot-and-Mouth Disease Virus AISTATS 2014 Sparse Factor Analysis for Learning and Content Analytics JMLR 2014 Sparsity and the Truncated l^2-norm AISTATS 2014 Spectral Methods for Indian Buffet Process Inference NeurIPS 2014 Spherical Hamiltonian Monte Carlo for Constrained Target Distributions ICML 2014 Stochastic Gradient Hamiltonian Monte Carlo ICML 2014 Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices ICML 2014 Stochastic Variational Inference for Bayesian Time Series Models ICML 2014 Stochastic variational inference for hidden Markov models NeurIPS 2014 Testing Unfaithful Gaussian Graphical Models NeurIPS 2014 The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification NeurIPS 2014 The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling AISTATS 2014 The Infinite Mixture of Infinite Gaussian Mixtures NeurIPS 2014