We show how a generic feature selection algorithm returning strongly relevant variables can be turned into a causal structure learning algorithm. We prove this under the Faithfuln...
Causal reasoning is primarily concerned with what would happen to a system under external interventions. In particular, we are often interested in predicting the probability distr...
We present a scalable framework for parallelizing greedy graph coloring algorithms on distributed-memory computers. The framework unifies several existing algorithms and blends a ...
Doruk Bozdag, Assefaw Hadish Gebremedhin, Fredrik ...
Abstract-- Creating high quality network trace files is a difficult task to accomplish on a limited budget. High network speeds may overburden an individual system running packet l...
Chad D. Mano, Jeff Smith, Bill Bordogna, Andrew Ma...
We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...