Probabilistic Modeling

MAP Estimation for Graphical Models by Likelihood Maximization NeurIPS 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 Nonparametric Bayesian Matrix Factorization by Power-EP AISTATS 2010 On Combining Graph-based Variance Reduction schemes AISTATS 2010 Parallelizable Sampling of Markov Random Fields AISTATS 2010 Probabilistic Belief Revision with Structural Constraints NeurIPS 2010 Probabilistic Deterministic Infinite Automata NeurIPS 2010 Probabilistic Inference and Differential Privacy NeurIPS 2010 Probabilistic Lane Estimation using Basis Curves RSS 2010 Probabilistic latent variable models for distinguishing between cause and effect NeurIPS 2010 Self-Paced Learning for Latent Variable Models NeurIPS 2010 Sequential Monte Carlo Samplers for Dirichlet Process Mixtures AISTATS 2010 Shadow Dirichlet for Restricted Probability Modeling NeurIPS 2010 Sparse Instrumental Variables (SPIV) for Genome-Wide Studies NeurIPS 2010 State-Space Inference and Learning with Gaussian Processes AISTATS 2010 Structured Determinantal Point Processes NeurIPS 2010 Switched Latent Force Models for Movement Segmentation NeurIPS 2010 Switching state space model for simultaneously estimating state transitions and nonstationary firing rates NeurIPS 2010 Synergies in learning words and their referents NeurIPS 2010 Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines AISTATS 2010 The Maximal Causes of Natural Scenes are Edge Filters NeurIPS 2010