Probabilistic visualisation of high-dimensional binary data
Proceedings of the 1998 conference on Advances in neural information processing systems II
Convex Optimization
A Variational Method for Learning Sparse and Overcomplete Representations
Neural Computation
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Assessing Approximate Inference for Binary Gaussian Process Classification
The Journal of Machine Learning Research
Expectation Consistent Approximate Inference
The Journal of Machine Learning Research
Compressed sensing and Bayesian experimental design
Proceedings of the 25th international conference on Machine learning
Bayesian Inference and Optimal Design for the Sparse Linear Model
The Journal of Machine Learning Research
Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches
ECML '07 Proceedings of the 18th European conference on Machine Learning
Bayesian Inference for Sparse Generalized Linear Models
ECML '07 Proceedings of the 18th European conference on Machine Learning
The variational gaussian approximation revisited
Neural Computation
Convex variational Bayesian inference for large scale generalized linear models
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Mean field theory for sigmoid belief networks
Journal of Artificial Intelligence Research
Gaussian Processes for Machine Learning (GPML) Toolbox
The Journal of Machine Learning Research
Approximate Riemannian Conjugate Gradient Learning for Fixed-Form Variational Bayes
The Journal of Machine Learning Research
Expectation propagation for approximate Bayesian inference
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Robust Gaussian Process Regression with a Student-t Likelihood
The Journal of Machine Learning Research
Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models
SIAM Journal on Imaging Sciences
Sparse Bayesian learning for basis selection
IEEE Transactions on Signal Processing
Bayesian Reasoning and Machine Learning
Bayesian Reasoning and Machine Learning
Variational Gaussian process classifiers
IEEE Transactions on Neural Networks
glm-ie: generalised linear models inference & estimation toolbox
The Journal of Machine Learning Research
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We investigate Gaussian Kullback-Leibler (G-KL) variational approximate inference techniques for Bayesian generalised linear models and various extensions. In particular we make the following novel contributions: sufficient conditions for which the G-KL objective is differentiable and convex are described; constrained parameterisations of Gaussian covariance that make G-KL methods fast and scalable are provided; the lower bound to the normalisation constant provided by G-KL methods is proven to dominate those provided by local lower bounding methods; complexity and model applicability issues of G-KL versus other Gaussian approximate inference methods are discussed. Numerical results comparing G-KL and other deterministic Gaussian approximate inference methods are presented for: robust Gaussian process regression models with either Student-t or Laplace likelihoods, large scale Bayesian binary logistic regression models, and Bayesian sparse linear models for sequential experimental design.