Bayesian learning, widely used in many applied data-modeling problems, is often accomplished with approximation schemes because it requires intractable computation of the posterio...
We study local, distributed algorithms for the capacitated minimum dominating set (CapMDS) problem, which arises in various distributed network applications. Given a network graph...
We present a novel message passing algorithm for approximating the MAP problem in graphical models. The algorithm is similar in structure to max-product but unlike max-product it ...
In many application domains there is a large amount of unlabeled data but only a very limited amount of labeled training data. One general approach that has been explored for util...
Avrim Blum, John D. Lafferty, Mugizi Robert Rweban...
The Steiner tree problem is one of the most fundamental ÆÈ-hard problems: given a weighted undirected graph and a subset of terminal nodes, find a minimum weight tree spanning ...
Jaroslaw Byrka, Fabrizio Grandoni, Thomas Rothvoss...