We propose a new algorithm for learning kernels for variants of the Normalized Cuts (NCuts) objective – i.e., given a set of training examples with known partitions, how should ...
Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in control theory, machine learning, and discrete geometry. This c...
The current best conformant probabilistic planners encode the problem as a bounded length CSP or SAT problem. While these approaches can find optimal solutions for given plan leng...
Daniel Bryce, Subbarao Kambhampati, David E. Smith
We present a novel portfolio selection technique, which replaces the traditional maximization of the utility function with a probabilistic approach inspired by statistical physics....
Robert Marschinski, Pietro Rossi, Massimo Tavoni, ...
We propose a framework for 3D geometry processing that provides direct access to surface curvature to facilitate advanced shape editing, filtering, and synthesis algorithms. The c...