We give the first black-box reduction from arbitrary approximation algorithms to truthful approximation mechanisms for a non-trivial class of multiparameter problems. Specifically,...
—We investigate approximating joint distributions of random processes with causal dependence tree distributions. Such distributions are particularly useful in providing parsimoni...
Christopher J. Quinn, Todd P. Coleman, Negar Kiyav...
We explore the relation between the stochastic semantic associated to stochastic Concurrent Constrain Programming (sCCP) and its fluid-flow approximation. Writing the master equat...
We will give a survey on results related to scheduling problems where machines are not continuously available for processing. We will deal with single and multi machine problems an...
We study the problem of minimizing the expected loss of a linear predictor while constraining its sparsity, i.e., bounding the number of features used by the predictor. While the r...