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NIPS
2003
13 years 9 months ago
Perspectives on Sparse Bayesian Learning
Recently, relevance vector machines (RVM) have been fashioned from a sparse Bayesian learning (SBL) framework to perform supervised learning using a weight prior that encourages s...
David P. Wipf, Jason A. Palmer, Bhaskar D. Rao
CDC
2009
IEEE
124views Control Systems» more  CDC 2009»
13 years 5 months ago
A graph-theoretic approach to distributed control over networks
We consider a network of control systems connected over a graph. Considering the graph structure as constraints on the set of permissible controllers, we show that such systems ar...
John Swigart, Sanjay Lall
SIAMIS
2010
156views more  SIAMIS 2010»
13 years 2 months ago
Learning the Morphological Diversity
This article proposes a new method for image separation into a linear combination of morphological components. Sparsity in fixed dictionaries is used to extract the cartoon and osc...
Gabriel Peyré, Jalal Fadili, Jean-Luc Starc...
JMLR
2011
192views more  JMLR 2011»
13 years 3 months ago
Minimum Description Length Penalization for Group and Multi-Task Sparse Learning
We propose a framework MIC (Multiple Inclusion Criterion) for learning sparse models based on the information theoretic Minimum Description Length (MDL) principle. MIC provides an...
Paramveer S. Dhillon, Dean P. Foster, Lyle H. Unga...
JMLR
2012
11 years 10 months ago
On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models
This paper studies issues relating to the parameterization of probability distributions over binary data sets. Several such parameterizations of models for binary data are known, ...
David Buchman, Mark W. Schmidt, Shakir Mohamed, Da...