Probabilistic inductive logic programming, sometimes also called statistical relational learning, addresses one of the central questions of artificial intelligence: the integratio...
In many applications, users specify target values for certain attributes, without requiring exact matches to these values in return. Instead, the result to such queries is typical...
We develop a framework for analyzing security protocols in which protocol adversaries may be arbitrary probabilistic polynomial-time processes. In this framework, protocols are wr...
Patrick Lincoln, John C. Mitchell, Mark Mitchell, ...
We present the process that has been followed for the development of an application that makes use of several heterogeneous Spanish public datasets that are related to administrat...
We are interested in learning programs for multiple related tasks given only a few training examples per task. Since the program for a single task is underdetermined by its data, ...