Probabilistic Modeling

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 Interval Tracker: Tracking by Interval Analysis CVPR 2014 Iterative Multilevel MRF Leveraging Context and Voxel Information for Brain Tumour Segmentation in MRI CVPR 2014 Latent Confusion Analysis by Normalized Gamma Construction ICML 2014 Latent Gaussian Models for Topic Modeling AISTATS 2014 Learning Heterogeneous Hidden Markov Random Fields AISTATS 2014 Learning Latent Variable Gaussian Graphical Models ICML 2014 Learning Mixtures of Ranking Models NeurIPS 2014 Learning Time-Varying Coverage Functions NeurIPS 2014 Learning with Maximum A-Posteriori Perturbation Models AISTATS 2014 Lifted MAP Inference for Markov Logic Networks AISTATS 2014 Loopy Belief Propagation in the Presence of Determinism AISTATS 2014 Low-density Parity Constraints for Hashing-Based Discrete Integration ICML 2014 Modeling Correlated Arrival Events with Latent Semi-Markov Processes ICML 2014 Neural Variational Inference and Learning in Belief Networks ICML 2014 Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks NeurIPS 2014 New Rules for Domain Independent Lifted MAP Inference NeurIPS 2014 On Sparse Gaussian Chain Graph Models NeurIPS 2014 On the Testability of Models with Missing Data AISTATS 2014 Optimality of Thompson Sampling for Gaussian Bandits Depends on Priors AISTATS 2014 Particle Gibbs with Ancestor Sampling JMLR 2014 Probabilistic Matrix Factorization with Non-random Missing Data ICML 2014 Probabilistic ODE Solvers with Runge-Kutta Means NeurIPS 2014