Bayesian Experimental Design of Magnetic Resonance Imaging Sequences (2009)
Seeger, M.W., Nickisch, H., Pohmann, R., Schölkopf, B.
We show how improved sequences for magnetic resonance imaging can be found through automated optimization of Bayesian design scores. Combining recent advances in approximate Bayesian inference and...
Bayesian Inference and Optimal Design for the Sparse Linear Model (2008)
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maximum a posteriori...
Information Consistency of Nonparametric Gaussian Process Methods (2008)
Seeger, M.W., Kakade, S.M., Foster, D.P.
AbstractBayesian nonparametric models are widely and successfully used for statistical prediction. While posterior consistency properties are well studied in quite general settings, results have...
Compressed Sensing and Bayesian Experimental Design (2008)
Seeger, M.W., Nickisch, H., Cohen, W. W., McCallum, A., Roweis, S.
We relate compressed sensing (CS) with Bayesian experimental design and provide a novel efficient approximate method for the latter, based on expectation propagation. In a large comparative study...