Bayesian & Probabilistic

Generating Efficient MCMC Kernels from Probabilistic Programs AISTATS 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 Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models NeurIPS 2014 Hardness of parameter estimation in graphical models NeurIPS 2014 Hybrid Discriminative-Generative Approach with Gaussian Processes AISTATS 2014 Improved Multimodal Deep Learning with Variation of Information NeurIPS 2014 Improving Prediction from Dirichlet Process Mixtures via Enrichment JMLR 2014 Incremental Local Gaussian Regression NeurIPS 2014 Incremental Tree-Based Inference with Dependent Normalized Random Measures AISTATS 2014 Inference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers NeurIPS 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 Iterative Multilevel MRF Leveraging Context and Voxel Information for Brain Tumour Segmentation in MRI CVPR 2014 Kernel Mean Estimation via Spectral Filtering NeurIPS 2014 Latent Gaussian Models for Topic Modeling AISTATS 2014 Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data ICML 2014 Learning a Concept Hierarchy from Multi-labeled Documents NeurIPS 2014 Learning Chordal Markov Networks by Dynamic Programming NeurIPS 2014 Learning Graphs with a Few Hubs ICML 2014 Learning Latent Variable Gaussian Graphical Models ICML 2014 Learning Mixtures of Discrete Product Distributions using Spectral Decompositions COLT 2014 Learning Mixtures of Ranking Models NeurIPS 2014