Reformulating constraint satisfaction problems (CSPs) in lower arity is a common procedure when computing consistency. Lower arity CSPs are simpler to treat than high arity CSPs. ...
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computat...
Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller,...
Variable selection is an important and practical problem that arises in analysis of many high-dimensional datasets. Convex optimization procedures that arise from relaxing the NP-...
We define a generalized variant of the satisfiability problem (SAT) where each "clause" is an or-list of inequalities in n variables. The inequality satisfiability probl...
Data Mining with Bayesian Network learning has two important characteristics: under broad conditions learned edges between variables correspond to causal influences, and second, f...
Ioannis Tsamardinos, Constantin F. Aliferis, Alexa...