Recent research has studied the role of sparsity in high dimensional regression and signal reconstruction, establishing theoretical limits for recovering sparse models from sparse...
Shuheng Zhou, John D. Lafferty, Larry A. Wasserman
In this paper we examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly ...
In this paper, we extend the traditional formalization of a Distributed Constraint Satisfaction Problems (DisCSP) to a Quantified DisCSP. A Quantified DisCSP includes several unive...
An intuitive approach to utilizing unlabeled data in kernel-based classification algorithms is to simply treat unknown labels as additional optimization variables. For marginbased...
Vikas Sindhwani, S. Sathiya Keerthi, Olivier Chape...
In this overview we show how Knowledge Representation (KR) can be done with the help of generalized logic programs. We start by introducing the core of PROLOG, which is based on de...