Probabilistic Modeling

Joint Modeling of a Matrix with Associated Text via Latent Binary Features NeurIPS 2012 Kernel Topic Models AISTATS 2012 Label Ranking with Partial Abstention based on Thresholded Probabilistic Models NeurIPS 2012 Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning NeurIPS 2012 Latent Graphical Model Selection: Efficient Methods for Locally Tree-like Graphs NeurIPS 2012 Learning as MAP Inference in Discrete Graphical Models NeurIPS 2012 Learning from Distributions via Support Measure Machines NeurIPS 2012 Learning From Ordered Sets and Applications in Collaborative Ranking ACML 2012 Learning from the Wisdom of Crowds by Minimax Entropy NeurIPS 2012 Learning Label Trees for Probabilistic Modelling of Implicit Feedback NeurIPS 2012 Learning Latent Variable Models by Pairwise Cluster Comparison ACML 2012 Learning Mixtures of Tree Graphical Models NeurIPS 2012 Learning Probability Measures with respect to Optimal Transport Metrics NeurIPS 2012 Learning to Discover Social Circles in Ego Networks NeurIPS 2012 Lifted Linear Programming AISTATS 2012 Lifted Variable Elimination with Arbitrary Constraints AISTATS 2012 Lightning-speed Structure Learning of Nonlinear Continuous Networks AISTATS 2012 Low rank continuous-space graphical models AISTATS 2012 Majorization for CRFs and Latent Likelihoods NeurIPS 2012 MAP Inference in Chains using Column Generation NeurIPS 2012 Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data AISTATS 2012 Meta-Gaussian Information Bottleneck NeurIPS 2012 Minimization of Continuous Bethe Approximations: A Positive Variation NeurIPS 2012 Mixing Properties of Conditional Markov Chains with Unbounded Feature Functions NeurIPS 2012 Modelling Reciprocating Relationships with Hawkes Processes NeurIPS 2012