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Bayesian Inference

4821 directly classified papers

Papers per year

Papers

Stochastic Gradient Hamiltonian Monte Carlo ICML 2014
A Compilation Target for Probabilistic Programming Languages ICML 2014
Analysis of Empirical MAP and Empirical Partially Bayes: Can They be Alternatives to Variational Bayes? AISTATS 2014
PAC-Bayesian Theory for Transductive Learning AISTATS 2014
PAC-Bayesian Collective Stability AISTATS 2014
Scaling Nonparametric Bayesian Inference via Subsample-Annealing AISTATS 2014
Accelerating ABC methods using Gaussian processes AISTATS 2014
Generating Efficient MCMC Kernels from Probabilistic Programs AISTATS 2014
Latent Gaussian Models for Topic Modeling AISTATS 2014
A Gaussian Latent Variable Model for Large Margin Classification of Labeled and Unlabeled Data AISTATS 2014
Deterministic Anytime Inference for Stochastic Continuous-Time Markov Processes ICML 2014
Learning with Maximum A-Posteriori Perturbation Models AISTATS 2014
The Dependent Dirichlet Process Mixture of Objects for Detection-free Tracking and Object Modeling AISTATS 2014
Improving Prediction from Dirichlet Process Mixtures via Enrichment JMLR 2014
Distributed Bayesian Posterior Sampling via Moment Sharing NIPS 2014
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature NIPS 2014
Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models NIPS 2014
Nonparametric Bayesian inference on multivariate exponential families NIPS 2014
Bayesian Sampling Using Stochastic Gradient Thermostats NIPS 2014
Scalable Inference for Neuronal Connectivity from Calcium Imaging NIPS 2014
Sparse Bayesian structure learning with “dependent relevance determination” priors NIPS 2014
A Bayesian Framework For the Local Configuration of Retinal Junctions CVPR 2014
Positive Curvature and Hamiltonian Monte Carlo NIPS 2014
Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights NIPS 2014
Adaptive Monte Carlo via Bandit Allocation ICML 2014