Bayesian learning, widely used in many applied data-modeling problems, is often accomplished with approximation schemes because it requires intractable computation of the posterio...
The main goal of this article is to discuss a controllability approach to the image matching / shape identification problem, an important issue in many applications, medical ones ...
Histograms are typically used to approximate data distributions. Histograms and related synopsis structures have been successful in a wide variety of popular database applications...
We improve the approximation ratios for two optimization problems in planar graphs. For node-weighted Steiner tree, a classical network-optimization problem, the best achievable ap...
Erik D. Demaine, MohammadTaghi Hajiaghayi, Philip ...
In many structured prediction problems, the highest-scoring labeling is hard to compute exactly, leading to the use of approximate inference methods. However, when inference is us...