Code

More recent code can be found in the github pages.

Older Code

• GLOMO: MATLAB Toolbox for learning layers from video.

• MATLAB software on sampling in Gaussian processes using control variables. Control points algorithm described in the paper: Efficient Sampling for Gaussian Process Inference using Control Variables, NIPS 2009. The software requires the minimize.m function of Carl. Rasmussen.

• Software on sparse variational GPs. The method described in the paper: Variational Learning of Inducing Variables in Sparse Gaussian Processes. AISTATS, 2009.

• Code for variational sparse linear models. Based on the paper: Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning, NIPS, 2012. It can deal with: sparse linear regression; sparse factor analysis and PCA; multi-output Gaussian process regression and more.

• Code for Variational Inference over kernel GP hyperparameters. Based on the paper: Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression, NIPS, 2013.

• Code for doubly stochastic variational inference. Based on the paper: Doubly Stochastic Variational Bayes for non-Conjugate Inference, ICML, 2014. Examples on automatic variable/feature selection in Bayesian logistic regression and Gaussian process hyperparameters inference are included.

• Code applying local expectation gradients to sigmoid belief nets. Based on the paper: Local Expectation Gradients for Black Box Variational Inference, NIPS, 2015. Examples for learning sigmoid belief nets on MNIST are included.

• Variational GP-LVM code. (written together with Andreas C. Damianou and Neil D. Lawrence)

• Unbiased Implicit Variational Inference (UIVI). Based on the paper: Unbiased Implicit Variational Inference. AISTATS, 2018. (written together with Francisco J. R. Ruiz)

• Code to minimize the Variational Contrastive Divergence (VCD). Based on the paper: A Contrastive Divergence for Combining Variational Inference and MCMC. ICML. 2019. (written together with Francisco J. R. Ruiz)