Abstract. We propose a dynamic process for network evolution, aiming at explaining the emergence of the small world phenomenon, i.e., the statistical observation that any pair of i...
Augustin Chaintreau, Pierre Fraigniaud, Emmanuelle...
Abstract. To solve problems that require far more memory than a single machine can supply, data can be swapped to disk in some manner, it can be compressed, and/or the memory of mu...
We study the properties of the agnostic learning framework of Haussler [Hau92] and Kearns, Schapire and Sellie [KSS94]. In particular, we address the question: is there any situat...
Confidence measures for the generalization error are crucial when small training samples are used to construct classifiers. A common approach is to estimate the generalization err...
A large class of stochastic optimization problems can be modeled as minimizing an objective function f that depends on a choice of a vector x ∈ X, as well as on a random external...