Probabilistic Modeling

The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models JMLR 2009 The 'tree-dependent components' of natural scenes are edge filters NeurIPS 2009 Toward Provably Correct Feature Selection in Arbitrary Domains NeurIPS 2009 Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference NeurIPS 2009 Which graphical models are difficult to learn? NeurIPS 2009 Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise NeurIPS 2009 Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes NeurIPS 2008 A general framework for investigating how far the decoding process in the brain can be simplified NeurIPS 2008 A mixture model for the evolution of gene expression in non-homogeneous datasets NeurIPS 2008 An ideal observer model of infant object perception NeurIPS 2008 Approximations for Binary Gaussian Process Classification JMLR 2008 Bayesian Exponential Family PCA NeurIPS 2008 Bayesian Network Score Approximation using a Metagraph Kernel NeurIPS 2008 Bounds on marginal probability distributions NeurIPS 2008 Causal Reasoning with Ancestral Graphs JMLR 2008 Characterizing response behavior in multisensory perception with conflicting cues NeurIPS 2008 Continuously-adaptive discretization for message-passing algorithms NeurIPS 2008 Counting Solution Clusters in Graph Coloring Problems Using Belief Propagation NeurIPS 2008 Detection of Principle Directions in Unknown Environments for Autonomous Navigation RSS 2008 Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction JMLR 2008 Efficient Exact Inference in Planar Ising Models NeurIPS 2008 Evaluating probabilities under high-dimensional latent variable models NeurIPS 2008 Extracting State Transition Dynamics from Multiple Spike Trains with Correlated Poisson HMM NeurIPS 2008 Gas Distribution Modeling using Sparse Gaussian Process Mixture Models RSS 2008 Gates NeurIPS 2008