expectation propagation

Gaussian Process Vine Copulas for Multivariate Dependence ICML 2013 Generalized Spike-and-Slab Priors for Bayesian Group Feature Selection Using Expectation Propagation JMLR 2013 Learning Feature Selection Dependencies in Multi-task Learning NeurIPS 2013 Learning to Pass Expectation Propagation Messages NeurIPS 2013 Message passing with l1 penalized KL minimization ICML 2013 Nested Expectation Propagation for Gaussian Process Classification with a Multinomial Probit Likelihood JMLR 2013 Perturbative Corrections for Approximate Inference in Gaussian Latent Variable Models JMLR 2013 Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables AISTATS 2013 Whitened Expectation Propagation: Non-Lambertian Shape from Shading and Shadow CVPR 2013 Collaborative Gaussian Processes for Preference Learning NeurIPS 2012 Expectation Propagation in Gaussian Process Dynamical Systems NeurIPS 2012 glm-ie: Generalised Linear Models Inference & Estimation Toolbox JMLR 2012 Minimization of Continuous Bethe Approximations: A Positive Variation NeurIPS 2012 An Application of Tree-Structured Expectation Propagation for Channel Decoding NeurIPS 2011 Approximate Marginals in Latent Gaussian Models JMLR 2011 Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference AISTATS 2011 Probabilistic amplitude and frequency demodulation NeurIPS 2011 Robust Gaussian Process Regression with a Student- Likelihood JMLR 2011 Robust Multi-Class Gaussian Process Classification NeurIPS 2011 Bayesian Online Learning for Multi-label and Multi-variate Performance Measures AISTATS 2010 Boosted Optimization for Network Classification AISTATS 2010 Coherent Inference on Optimal Play in Game Trees AISTATS 2010 Gaussian Processes for Machine Learning (GPML) Toolbox JMLR 2010 Gaussian processes with monotonicity information AISTATS 2010 Improving posterior marginal approximations in latent Gaussian models AISTATS 2010