Much work on skewed, stochastic, high dimensional, and biased datasets usually implicitly solve each problem separately. Recently, we have been approached by Texas Commission on En...
Probabilistic inference techniques can be used to estimate variable bias, or the proportion of solutions to a given SAT problem that fix a variable positively or negatively. Metho...
Covering and packing integer programs model a large family of combinatorial optimization problems. The current-best approximation algorithms for these are an instance of the basic...
We present a novel approach to the matching of subgraphs for object recognition in computer vision. Feature similarities between object model and scene graph are complemented with ...
Probabilistic combinatorial games (PCG) are a model for Go-like games recently introduced by Ken Chen. They differ from normal combinatorial games since terminal position in each ...