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Information-based objective functions for active data selection
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Fundamentals of speech recognition
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On-line learning in neural networks
Atomic Decomposition by Basis Pursuit
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An introduction to variational methods for graphical models
Learning in graphical models
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Mean-field approaches to independent component analysis
Neural Computation
Expectation Propagation for approximate Bayesian inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Variational methods for inference and estimation in graphical models
Variational methods for inference and estimation in graphical models
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Adaptive Sparseness for Supervised Learning
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Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Convex Optimization
Predictive automatic relevance determination by expectation propagation
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The Entire Regularization Path for the Support Vector Machine
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A Variational Method for Learning Sparse and Overcomplete Representations
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Expectation Consistent Approximate Inference
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Bayesian Inference for Sparse Generalized Linear Models
ECML '07 Proceedings of the 18th European conference on Machine Learning
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Compressed sensing and Bayesian experimental design
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A Sparse Regression Mixture Model for Clustering Time-Series
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
Convex variational Bayesian inference for large scale generalized linear models
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
The Bayesian group-Lasso for analyzing contingency tables
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Model Selection: Beyond the Bayesian/Frequentist Divide
The Journal of Machine Learning Research
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UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Expectation Propagation for microarray data classification
Pattern Recognition Letters
Network-based sparse Bayesian classification
Pattern Recognition
Lp-Nested Symmetric Distributions
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Approximate Marginals in Latent Gaussian Models
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Properties of Bethe free energies and message passing in Gaussian models
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Robust Gaussian Process Regression with a Student-t Likelihood
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Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models
SIAM Journal on Imaging Sciences
A sparse spatial linear regression model for fMRI data analysis
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Optimization with Sparsity-Inducing Penalties
Foundations and Trends® in Machine Learning
Closed-Form EM for sparse coding and its application to source separation
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
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Pattern Recognition Letters
Sparse regression learning by aggregation and Langevin Monte-Carlo
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Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Value function approximation through sparse bayesian modeling
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Hypergraph spectra for semi-supervised feature selection
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Efficiently learning the preferences of people
Machine Learning
Sparsity regret bounds for individual sequences in online linear regression
The Journal of Machine Learning Research
Journal of Computational Physics
Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation
The Journal of Machine Learning Research
Gaussian Kullback-Leibler approximate inference
The Journal of Machine Learning Research
Personal and Ubiquitous Computing
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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 sparse solutions and neglect to represent posterior uncertainties. In this paper, we address problems of Bayesian optimal design (or experiment planning), for which accurate estimates of uncertainty are essential. To this end, we employ expectation propagation approximate inference for the linear model with Laplace prior, giving new insight into numerical stability properties and proposing a robust algorithm. We also show how to estimate model hyperparameters by empirical Bayesian maximisation of the marginal likelihood, and propose ideas in order to scale up the method to very large underdetermined problems. We demonstrate the versatility of our framework on the application of gene regulatory network identification from micro-array expression data, where both the Laplace prior and the active experimental design approach are shown to result in significant improvements. We also address the problem of sparse coding of natural images, and show how our framework can be used for compressive sensing tasks. Part of this work appeared in Seeger et al. (2007b). The gene network identification application appears in Steinke et al. (2007).