We consider the problem of learning density mixture models for classification. Traditional learning of mixtures for density estimation focuses on models that correctly represent t...
We present a new method for nonlinear prediction of discrete random sequences under minimal structural assumptions. We give a mathematical construction for optimal predictors of s...
We present a sound and complete proof technique, based on syntactic logical relations, for showing contextual equivalence of expressions in a -calculus with recursive types and imp...
Many decidability results are known for non-recursive cryptographic protocols, where the protocol steps can be expressed by simple rewriting rules. Recently, a tree transducer-base...
We provide machine-independent characterizations of some complexity classes, over an arbitrary structure, in the model of computation proposed by L. Blum, M. Shub and S. Smale. We...
Olivier Bournez, Felipe Cucker, Paulin Jacob&eacut...