Bayesian Learning

Multi-Task Learning for Classification with Dirichlet Process Priors JMLR 2007 Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach NeurIPS 2007 Robust Regression with Twinned Gaussian Processes NeurIPS 2007 Semi-Supervised Multitask Learning NeurIPS 2007 The Infinite Gamma-Poisson Feature Model NeurIPS 2007 The Infinite Markov Model NeurIPS 2007 Accelerated Variational Dirichlet Process Mixtures NeurIPS 2006 Adaptor Grammars: A Framework for Specifying Compositional Nonparametric Bayesian Models NeurIPS 2006 A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments NeurIPS 2006 A PAC-Bayes Risk Bound for General Loss Functions NeurIPS 2006 Bayesian Ensemble Learning NeurIPS 2006 Bayesian Image Super-resolution, Continued NeurIPS 2006 Bayesian Network Learning with Parameter Constraints JMLR 2006 Bayesian Policy Gradient Algorithms NeurIPS 2006 Context Effects in Category Learning: An Investigation of Four Probabilistic Models NeurIPS 2006 Data Integration for Classification Problems Employing Gaussian Process Priors NeurIPS 2006 Dirichlet-Enhanced Spam Filtering based on Biased Samples NeurIPS 2006 Hierarchical Dirichlet Processes with Random Effects NeurIPS 2006 Learning annotated hierarchies from relational data NeurIPS 2006 Learning Structural Equation Models for fMRI NeurIPS 2006 Learning Time-Intensity Profiles of Human Activity using Non-Parametric Bayesian Models NeurIPS 2006 Learning to be Bayesian without Supervision NeurIPS 2006 Modeling Dyadic Data with Binary Latent Factors NeurIPS 2006 PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier NeurIPS 2006 Particle Filtering for Nonparametric Bayesian Matrix Factorization NeurIPS 2006