Bayesian & Probabilistic

Information Rates and Optimal Decoding in Large Neural Populations NeurIPS 2011 Inverting Grice's Maxims to Learn Rules from Natural Language Extractions NeurIPS 2011 Kernel Bayes' Rule NeurIPS 2011 Kernel Belief Propagation AISTATS 2011 Kernel Embeddings of Latent Tree Graphical Models NeurIPS 2011 Learning Higher-Order Graph Structure with Features by Structure Penalty NeurIPS 2011 Learning mixtures of Gaussians with maximum-a-posteriori oracle 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 Message-Passing for Approximate MAP Inference with Latent Variables NeurIPS 2011 Mixed Cumulative Distribution Networks AISTATS 2011 Mixed-Variate Restricted Boltzmann Machines ACML 2011 Modelling Genetic Variations using Fragmentation-Coagulation Processes NeurIPS 2011 Multiscale Community Blockmodel for Network Exploration AISTATS 2011 Neural Reconstruction with Approximate Message Passing (NeuRAMP) NeurIPS 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 Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation NeurIPS 2011 Nonstandard Interpretations of Probabilistic Programs for Efficient Inference NeurIPS 2011 On Causal Discovery with Cyclic Additive Noise Models NeurIPS 2011 On the Analysis of Multi-Channel Neural Spike Data NeurIPS 2011 On Time Varying Undirected Graphs AISTATS 2011 On Tracking The Partition Function NeurIPS 2011