Bayesian & Probabilistic

Kernel Embeddings of Latent Tree Graphical Models NeurIPS 2011 Learning equivalence classes of acyclic models with latent and selection variables from multiple datasets with overlapping variables AISTATS 2011 Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities NeurIPS 2011 Learning to Learn with Compound HD Models NeurIPS 2011 Learning unbelievable probabilities NeurIPS 2011 Logistic Stick-Breaking Process JMLR 2011 MAP Inference for Bayesian Inverse Reinforcement Learning NeurIPS 2011 Message-Passing for Approximate MAP Inference with Latent Variables NeurIPS 2011 Mixed Cumulative Distribution Networks AISTATS 2011 Modelling Genetic Variations using Fragmentation-Coagulation Processes NeurIPS 2011 Monte Carlo Pose Estimation with Quaternion Kernels and the Bingham Distribution RSS 2011 Multiscale Community Blockmodel for Network Exploration AISTATS 2011 Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability NeurIPS 2011 Non-conjugate Variational Message Passing for Multinomial and Binary Regression NeurIPS 2011 Nonlinear Inverse Reinforcement Learning with Gaussian Processes NeurIPS 2011 Non-Parametric Estimation of Topic Hierarchies from Texts with Hierarchical Dirichlet Processes JMLR 2011 Nonstandard Interpretations of Probabilistic Programs for Efficient Inference NeurIPS 2011 On the Analysis of Multi-Channel Neural Spike Data NeurIPS 2011 On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference NeurIPS 2011 On Tracking The Partition Function NeurIPS 2011 Optimal Reinforcement Learning for Gaussian Systems NeurIPS 2011 Parallel Algorithm for Learning Optimal Bayesian Network Structure JMLR 2011 Polytope samplers for inference in ill-posed inverse problems AISTATS 2011 Practical Variational Inference for Neural Networks NeurIPS 2011 Predicting response time and error rates in visual search NeurIPS 2011