Bayesian & Probabilistic

Efficient Inference for Complex Queries on Complex Distributions AISTATS 2014 Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials NeurIPS 2014 Efficient Nonlinear Markov Models for Human Motion CVPR 2014 Elementary Estimators for Graphical Models NeurIPS 2014 Estimating Latent-Variable Graphical Models using Moments and Likelihoods ICML 2014 Exchangeable Variable Models ICML 2014 Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights NeurIPS 2014 Expectation-Maximization for Learning Determinantal Point Processes NeurIPS 2014 Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables AISTATS 2014 Exploiting the Limits of Structure Learning via Inherent Symmetry AISTATS 2014 Extended and Unscented Gaussian Processes NeurIPS 2014 Extracting Latent Structure From Multiple Interacting Neural Populations NeurIPS 2014 Fast Allocation of Gaussian Process Experts ICML 2014 Faster and Sample Near-Optimal Algorithms for Proper Learning Mixtures of Gaussians COLT 2014 Fast Kernel Learning for Multidimensional Pattern Extrapolation NeurIPS 2014 Fast Sampling-Based Inference in Balanced Neuronal Networks NeurIPS 2014 Feedforward Learning of Mixture Models NeurIPS 2014 From MAP to Marginals: Variational Inference in Bayesian Submodular Models NeurIPS 2014 From Stochastic Grammar to Bayes Network: Probabilistic Parsing of Complex Activity CVPR 2014 Gaussian Approximation of Collective Graphical Models ICML 2014 Gaussian Process Classification and Active Learning with Multiple Annotators ICML 2014 Gaussian Processes for Bayesian Estimation in Ordinary Differential Equations ICML 2014 Gaussian Process Optimization with Mutual Information ICML 2014 Gaussian Process Volatility Model NeurIPS 2014 General Table Completion using a Bayesian Nonparametric Model NeurIPS 2014