Bayesian & Probabilistic

Fast Allocation of Gaussian Process Experts ICML 2014 Fast Sampling-Based Inference in Balanced Neuronal Networks NeurIPS 2014 From MAP to Marginals: Variational Inference in Bayesian Submodular Models NeurIPS 2014 Fully Simplified Multivariate Normal Updates in Non-Conjugate Variational Message Passing JMLR 2014 Gaussian Approximation of Collective Graphical Models ICML 2014 Gaussian Process Classification and Active Learning with Multiple Annotators ICML 2014 Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations ICML 2014 Gaussian Process Volatility Model NeurIPS 2014 General Table Completion using a Bayesian Nonparametric Model NeurIPS 2014 Generating Efficient MCMC Kernels from Probabilistic Programs AISTATS 2014 Gibbs Max-margin Topic Models with Data Augmentation JMLR 2014 Global Sensitivity Analysis for MAP Inference in Graphical Models NeurIPS 2014 Graph-based Semi-supervised Learning: Realizing Pointwise Smoothness Probabilistically ICML 2014 Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data NeurIPS 2014 Ground Plane Estimation using a Hidden Markov Model CVPR 2014 Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models NeurIPS 2014 Hardness of parameter estimation in graphical models NeurIPS 2014 Hierarchical Dirichlet Scaling Process ICML 2014 Hybrid Discriminative-Generative Approach with Gaussian Processes AISTATS 2014 Inconsistency of Pitman-Yor Process Mixtures for the Number of Components JMLR 2014 Incremental Local Gaussian Regression NeurIPS 2014 Incremental Tree-Based Inference with Dependent Normalized Random Measures AISTATS 2014 Inferning with High Girth Graphical Models ICML 2014 Inferring synaptic conductances from spike trains with a biophysically inspired point process model NeurIPS 2014 Information-based learning by agents in unbounded state spaces NeurIPS 2014