In this paper we provide theoretical and numerical analysis of a geometric activity flow network model which is aimed at explaining mathematically the scale-free functional graph s...
Bayesian learning, widely used in many applied data-modeling problems, is often accomplished with approximation schemes because it requires intractable computation of the posterio...
This paper defines PolyAML, a typed functional, aspect-oriented programming language. The main contribution of PolyAML is the seamless integration of polymorphism, run-time type a...
Daniel S. Dantas, David Walker, Geoffrey Washburn,...
Matching dependencies were recently introduced as declarative rules for data cleaning and entity resolution. Enforcing a matching dependency on a database instance identifies the ...
Leopoldo E. Bertossi, Solmaz Kolahi, Laks V. S. La...
We describe a slightly subexponential time algorithm for learning parity functions in the presence of random classification noise, a problem closely related to several cryptograph...