Bayesian & Probabilistic

Improving Human Judgments by Decontaminating Sequential Dependencies NeurIPS 2010 Improving posterior marginal approximations in latent Gaussian models AISTATS 2010 Improving the Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices NeurIPS 2010 Inference and Learning in Networks of Queues AISTATS 2010 Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks AISTATS 2010 Inference with Multivariate Heavy-Tails in Linear Models NeurIPS 2010 Infinite Predictor Subspace Models for Multitask Learning AISTATS 2010 Infinite Relational Modeling of Functional Connectivity in Resting State fMRI NeurIPS 2010 Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks NeurIPS 2010 Large Margin Learning of Upstream Scene Understanding Models NeurIPS 2010 Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development NeurIPS 2010 Layered image motion with explicit occlusions, temporal consistency, and depth ordering NeurIPS 2010 Learning Bayesian Network Structure using LP Relaxations AISTATS 2010 Learning concept graphs from text with stick-breaking priors NeurIPS 2010 Learning Efficient Markov Networks NeurIPS 2010 Learning invariant features using the Transformed Indian Buffet Process NeurIPS 2010 Learning Multiple Tasks with a Sparse Matrix-Normal Penalty NeurIPS 2010 Learning Networks of Stochastic Differential Equations NeurIPS 2010 Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors NeurIPS 2010 Learning the context of a category NeurIPS 2010 Learning via Gaussian Herding NeurIPS 2010 Learning with Blocks: Composite Likelihood and Contrastive Divergence AISTATS 2010 Lifted Inference Seen from the Other Side : The Tractable Features NeurIPS 2010 Linear readout from a neural population with partial correlation data NeurIPS 2010 MAP Estimation for Graphical Models by Likelihood Maximization NeurIPS 2010