← Optimization & Theory
Machine Learning › Optimization & Theory ›

Bayesian Inference

4821 directly classified papers

Papers per year

Papers

Rectangular Tiling Process ICML 2014
Pitfalls in the use of Parallel Inference for the Dirichlet Process ICML 2014
Bayesian Nonparametric Multilevel Clustering with Group-Level Contexts ICML 2014
Fast Allocation of Gaussian Process Experts ICML 2014
Sparse Factor Analysis for Learning and Content Analytics JMLR 2014
Locally Adaptive Factor Processes for Multivariate Time Series JMLR 2014
Thompson Sampling for Complex Online Problems ICML 2014
Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach ICML 2014
Spherical Hamiltonian Monte Carlo for Constrained Target Distributions ICML 2014
Efficient Continuous-Time Markov Chain Estimation ICML 2014
Learning Sum-Product Networks with Direct and Indirect Variable Interactions ICML 2014
Hamiltonian Monte Carlo Without Detailed Balance ICML 2014
Extended and Unscented Gaussian Processes NIPS 2014
Expectation-Maximization for Learning Determinantal Point Processes NIPS 2014
Bayesian Inference for Structured Spike and Slab Priors NIPS 2014
Asynchronous Anytime Sequential Monte Carlo NIPS 2014
Tree-structured Gaussian Process Approximations NIPS 2014
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data NIPS 2014
Projecting Markov Random Field Parameters for Fast Mixing NIPS 2014
Automated Variational Inference for Gaussian Process Models NIPS 2014
Bayes-Adaptive Simulation-based Search with Value Function Approximation NIPS 2014
Semi-supervised Learning with Deep Generative Models NIPS 2014
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions NIPS 2014
Agnostic Bayesian Learning of Ensembles ICML 2014
Learning the Parameters of Determinantal Point Process Kernels ICML 2014