In apprenticeship learning, the goal is to learn a policy in a Markov decision process that is at least as good as a policy demonstrated by an expert. The difficulty arises in tha...
This paper presents an approach to build Sparse Large Margin Classifiers (SLMC) by adding one more constraint to the standard Support Vector Machine (SVM) training problem. The ad...
Motivated by the problem of customer wallet estimation, we propose a new setting for multi-view regression, where we learn a completely unobserved target (in our case, customer wa...
We present a decomposition strategy to speed up constraint optimization for a representative multiprocessor scheduling problem. In the manner of Benders decomposition, our techniq...
Operations research and management science are often confronted with sequential decision making problems with large state spaces. Standard methods that are used for solving such c...