Bayesian & Probabilistic

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 Online Bayesian Passive-Aggressive Learning ICML 2014 On Prior Distributions and Approximate Inference for Structured Variables NeurIPS 2014 On Sparse Gaussian Chain Graph Models NeurIPS 2014 On the Testability of Models with Missing Data AISTATS 2014 Optimality of Thompson Sampling for Gaussian Bandits Depends on Priors AISTATS 2014 Optimal prior-dependent neural population codes under shared input noise NeurIPS 2014 PAC-Bayesian AUC classification and scoring NeurIPS 2014 Parallel MCMC with Generalized Elliptical Slice Sampling JMLR 2014 Parallel Sampling of HDPs using Sub-Cluster Splits NeurIPS 2014 Particle Gibbs with Ancestor Sampling JMLR 2014 Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data NeurIPS 2014 Positive Curvature and Hamiltonian Monte Carlo NeurIPS 2014 Predictive Entropy Search for Efficient Global Optimization of Black-box Functions NeurIPS 2014 Probabilistic Matrix Factorization with Non-random Missing Data ICML 2014 Probabilistic ODE Solvers with Runge-Kutta Means NeurIPS 2014 Probabilistic Partial Canonical Correlation Analysis ICML 2014 Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics AISTATS 2014 Putting MRFs on a Tensor Train ICML 2014 Random Bayesian networks with bounded indegree AISTATS 2014 Recursive Inversion Models for Permutations NeurIPS 2014