Fundamentals of statistical exponential families: with applications in statistical decision theory
Fundamentals of statistical exponential families: with applications in statistical decision theory
Consistent Feature Selection for Pattern Recognition in Polynomial Time
The Journal of Machine Learning Research
A new class of upper bounds on the log partition function
IEEE Transactions on Information Theory
Group lasso with overlap and graph lasso
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
Sparse Bayesian Regression for Grouped Variables in Generalized Linear Models
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
Multi-task feature learning via efficient l2, 1-norm minimization
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Super-resolution with sparse mixing estimators
IEEE Transactions on Image Processing
Network-based sparse Bayesian classification
Pattern Recognition
The group-lasso: l1,∞regularization versus l1,2regularization
Proceedings of the 32nd DAGM conference on Pattern recognition
lp-Norm Multiple Kernel Learning
The Journal of Machine Learning Research
Functional brain imaging with M/EEG using structured sparsity in time-frequency dictionaries
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Inferring multiple graphical structures
Statistics and Computing
Convex and Network Flow Optimization for Structured Sparsity
The Journal of Machine Learning Research
Structured Variable Selection with Sparsity-Inducing Norms
The Journal of Machine Learning Research
Optimization with Sparsity-Inducing Penalties
Foundations and Trends® in Machine Learning
Structured sparsity via alternating direction methods
The Journal of Machine Learning Research
Computational Statistics & Data Analysis
Active learning via neighborhood reconstruction
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation
The Journal of Machine Learning Research
Journal of Global Optimization
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The Group-Lasso method for finding important explanatory factors suffers from the potential non-uniqueness of solutions and also from high computational costs. We formulate conditions for the uniqueness of Group-Lasso solutions which lead to an easily implementable test procedure that allows us to identify all potentially active groups. These results are used to derive an efficient algorithm that can deal with input dimensions in the millions and can approximate the solution path efficiently. The derived methods are applied to large-scale learning problems where they exhibit excellent performance and where the testing procedure helps to avoid misinterpretations of the solutions.