Bayesian & Probabilistic

Learning Optimal Bounded Treewidth Bayesian Networks via Maximum Satisfiability AISTATS 2014 Learning Sum-Product Networks with Direct and Indirect Variable Interactions ICML 2014 Learning with Maximum A-Posteriori Perturbation Models AISTATS 2014 Low-density Parity Constraints for Hashing-Based Discrete Integration ICML 2014 Low-dimensional models of neural population activity in sensory cortical circuits NeurIPS 2014 Low-Rank Time-Frequency Synthesis NeurIPS 2014 Making Pairwise Binary Graphical Models Attractive NeurIPS 2014 Max-Margin Infinite Hidden Markov Models ICML 2014 Message Passing Inference for Large Scale Graphical Models with High Order Potentials NeurIPS 2014 Mind the Nuisance: Gaussian Process Classification using Privileged Noise NeurIPS 2014 Mode Estimation for High Dimensional Discrete Tree Graphical Models NeurIPS 2014 Modeling Correlated Arrival Events with Latent Semi-Markov Processes ICML 2014 Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures NeurIPS 2014 Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks NeurIPS 2014 New Rules for Domain Independent Lifted MAP Inference NeurIPS 2014 Nonmyopic ε-Bayes-Optimal Active Learning of Gaussian Processes ICML 2014 Non-Parametric Bayesian Constrained Local Models CVPR 2014 Nonparametric Bayesian inference on multivariate exponential families NeurIPS 2014 Nonparametric Estimation of Multi-View Latent Variable Models ICML 2014 On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning AISTATS 2014 On Prior Distributions and Approximate Inference for Structured Variables NeurIPS 2014 On Sparse Gaussian Chain Graph Models NeurIPS 2014 On the Information Theoretic Limits of Learning Ising Models NeurIPS 2014 Open Problem: Finding Good Cascade Sampling Processes for the Network Inference Problem COLT 2014 Optimal decision-making with time-varying evidence reliability NeurIPS 2014