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Probabilistic Modeling

1884 directly classified papers

Papers per year

Papers

A Compilation Target for Probabilistic Programming Languages ICML 2014
Kernel Mean Estimation via Spectral Filtering NIPS 2014
Iterative Multilevel MRF Leveraging Context and Voxel Information for Brain Tumour Segmentation in MRI CVPR 2014
Efficient Nonlinear Markov Models for Human Motion CVPR 2014
Learning Optimal Bounded Treewidth Bayesian Networks via Maximum Satisfiability AISTATS 2014
Bayesian Logistic Gaussian Process Models for Dynamic Networks AISTATS 2014
Efficient Inference for Complex Queries on Complex Distributions AISTATS 2014
Learning with Maximum A-Posteriori Perturbation Models AISTATS 2014
Exploiting the Limits of Structure Learning via Inherent Symmetry AISTATS 2014
Latent Gaussian Models for Topic Modeling AISTATS 2014
Incremental Tree-Based Inference with Dependent Normalized Random Measures AISTATS 2014
The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling AISTATS 2014
Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables AISTATS 2014
Accelerating ABC methods using Gaussian processes AISTATS 2014
Generating Efficient MCMC Kernels from Probabilistic Programs AISTATS 2014
Probabilistic ODE Solvers with Runge-Kutta Means NIPS 2014
Global Sensitivity Analysis for MAP Inference in Graphical Models NIPS 2014
Testing Unfaithful Gaussian Graphical Models NIPS 2014
Learning Mixtures of Ranking Models NIPS 2014
Recursive Inversion Models for Permutations NIPS 2014
A framework for studying synaptic plasticity with neural spike train data NIPS 2014
Nonparametric Bayesian inference on multivariate exponential families NIPS 2014
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature NIPS 2014
Optimal decision-making with time-varying evidence reliability NIPS 2014
Automated Variational Inference for Gaussian Process Models NIPS 2014