We study approximations of optimization problems with probabilistic constraints in which the original distribution of the underlying random vector is replaced with an empirical dis...
A new robust matching method is proposed. The Progressive Sample Consensus (PROSAC) algorithm exploits the linear ordering defined on the set of correspondences by a similarity fu...
Abstract. Learning ranking functions is crucial for solving many problems, ranging from document retrieval to building recommendation systems based on an individual user’s prefer...
We consider the problem of estimating the covariance matrix of an observation vector, using heterogeneous training samples, i.e., samples whose covariance matrices are not exactly ...
Many unstructured peer to peer (P2P) systems rely on a Peer Sampling Service (PSS) that returns randomly sampled nodes from the population comprising the system. PSS protocols are...