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ESANN
2007
15 years 3 months ago
Exploring the causal order of binary variables via exponential hierarchies of Markov kernels
Abstract. We propose a new algorithm for estimating the causal structure that underlies the observed dependence among n (n ≥ 4) binary variables X1, . . . , Xn. Our inference pri...
Xiaohai Sun, Dominik Janzing
JMLR
2006
113views more  JMLR 2006»
15 years 2 months ago
Learning the Structure of Linear Latent Variable Models
We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause...
Ricardo Silva, Richard Scheines, Clark Glymour, Pe...
JMLR
2010
128views more  JMLR 2010»
14 years 9 months ago
Learning Causal Structure from Overlapping Variable Sets
We present an algorithm name cSAT+ for learning the causal structure in a domain from datasets measuring different variable sets. The algorithm outputs a graph with edges correspo...
Sofia Triantafilou, Ioannis Tsamardinos, Ioannis G...
ROBOCUP
2005
Springer
109views Robotics» more  ROBOCUP 2005»
15 years 8 months ago
Using the Max-Plus Algorithm for Multiagent Decision Making in Coordination Graphs
Abstract. Coordination graphs offer a tractable framework for cooperative multiagent decision making by decomposing the global payoff function into a sum of local terms. Each age...
Jelle R. Kok, Nikos A. Vlassis
ICGI
1998
Springer
15 years 6 months ago
Learning k-Variable Pattern Languages Efficiently Stochastically Finite on Average from Positive Data
Abstract. The present paper presents a new approach of how to convert Gold-style [4] learning in the limit into stochastically finite learning with high confidence. We illustrate t...
Peter Rossmanith, Thomas Zeugmann