Probabilistic Modeling

Sparse Inverse Covariance Estimation with Calibration NeurIPS 2013 Spectral Learning of Hidden Markov Models from Dynamic and Static Data ICML 2013 Spectral methods for neural characterization using generalized quadratic models NeurIPS 2013 Spike train entropy-rate estimation using hierarchical Dirichlet process priors NeurIPS 2013 Squared-loss Mutual Information Regularization: A Novel Information-theoretic Approach to Semi-supervised Learning ICML 2013 Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex NeurIPS 2013 Structure Learning of Mixed Graphical Models AISTATS 2013 Subproblem-Tree Calibration: A Unified Approach to Max-Product Message Passing ICML 2013 Summary Statistics for Partitionings and Feature Allocations NeurIPS 2013 Tracking People and Their Objects CVPR 2013 Tracking Time-varying Graphical Structure NeurIPS 2013 Universal models for binary spike patterns using centered Dirichlet processes NeurIPS 2013 Unsupervised Structure Learning of Stochastic And-Or Grammars NeurIPS 2013 Using multiple samples to learn mixture models NeurIPS 2013 What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach NeurIPS 2013 What do row and column marginals reveal about your dataset? NeurIPS 2013 When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity NeurIPS 2013 Σ-Optimality for Active Learning on Gaussian Random Fields NeurIPS 2013 A Better Way to Pretrain Deep Boltzmann Machines NeurIPS 2012 A Composite Likelihood View for Multi-Label Classification AISTATS 2012 A Conditional Multinomial Mixture Model for Superset Label Learning NeurIPS 2012 A Coupled Indian Buffet Process Model for Collaborative Filtering ACML 2012 Adaptive Metropolis with Online Relabeling AISTATS 2012 A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation NeurIPS 2012 Affine Independent Variational Inference NeurIPS 2012