A variety of computer vision problems can be optimally posed as Bayesian labeling in which the solution of a problem is dened as the maximum a posteriori (MAP) probability estimate...
This paper discusses the topic of dimensionality reduction for k-means clustering. We prove that any set of n points in d dimensions (rows in a matrix A ∈ Rn×d ) can be project...
We give the first black-box reduction from arbitrary approximation algorithms to truthful approximation mechanisms for a non-trivial class of multiparameter problems. Specifically,...
A variable-to-fixed length encoder partitions the source string into variable-length phrases that belong to a given and fixed dictionary. Tunstall, and independently Khodak, desig...
Michael Drmota, Yuriy A. Reznik, Wojciech Szpankow...
We describe an enhanced method for the selection of optimal sensor actions in a probabilistic state estimation framework. We apply this to the selection of optimal focal lengths f...
Benjamin Deutsch, Heinrich Niemann, Joachim Denzle...