• Publications
  • Influence
Consistent Feature Selection for Pattern Recognition in Polynomial Time
TLDR
It is proved that ALL-RELEVANT is much harder than MINIMAL-OPTIMAL and two consistent, polynomial-time algorithms are proposed to simplify feature selection in a wide range of machine learning tasks.
Growing Bayesian network models of gene networks from seed genes
TLDR
A new algorithm for learning BN models of gene networks from gene-expression data that provides the user with a window of radius R around S to look at the BN model of a gene network without having to exclude any gene in advance.
Learning Gaussian Graphical Models of Gene Networks with False Discovery Rate Control
  • J. Peña
  • Computer Science
    EvoBIO
  • 26 March 2008
TLDR
An algorithm aiming at controlling the FDR of edges when learning Gaussian graphical models (GGMs) is presented, particularly suitable when dealing with more nodes than samples, e.g. when learning GGMs of gene networks from gene expression data.
Scalable, Efficient and Correct Learning of Markov Boundaries Under the Faithfulness Assumption
We propose an algorithm for learning the Markov boundary of a random variable from data without having to learn a complete Bayesian network. The algorithm is correct under the faithfulness
Every LWF and AMP Chain Graph Originates from a Set of Causal Models
  • J. Peña
  • Computer Science
    ECSQARU
  • 10 December 2013
TLDR
Every chain graph is inclusion optimal wrt the intersection of the independence models represented by a set of directed and acyclic graphs under conditioning, which implies that the independence model represented by the chain graph can be accounted for by a sets of causal models that are subject to selection bias.
A computational model coupling mechanics and electrophysiology in spinal cord injury
TLDR
A new multiscale model of myelinated axon associating electrophysiological impairment to structural damage as a function of strain and strain rate is proposed, providing a link between mechanical trauma and subsequent functional deficits.
On Local Optima in Learning Bayesian Networks
TLDR
This paper proposes and evaluates the k-greedy equivalence search algorithm (KES) for learning Bayesian networks (BNs) from complete data and proves that under mild conditions KES asymptotically returns any inclusion optimal BN with nonzero probability.
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