Probabilistic Modeling

Inference with Multivariate Heavy-Tails in Linear Models NeurIPS 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 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 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 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 Matrix-Variate Dirichlet Process Mixture Models AISTATS 2010 Maximum-likelihood learning of cumulative distribution functions on graphs AISTATS 2010 Mixture of time-warped trajectory models for movement decoding NeurIPS 2010 Modeling annotator expertise: Learning when everybody knows a bit of something AISTATS 2010 More data means less inference: A pseudo-max approach to structured learning NeurIPS 2010 Movement extraction by detecting dynamics switches and repetitions NeurIPS 2010 Neural conditional random fields AISTATS 2010 Noise-contrastive estimation: A new estimation principle for unnormalized statistical models AISTATS 2010 Nonparametric prior for adaptive sparsity AISTATS 2010 On Combining Graph-based Variance Reduction schemes AISTATS 2010