Markov decision processes (MDPs) with discrete and continuous state and action components can be solved efficiently by hybrid approximate linear programming (HALP). The main idea ...
We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regulariza...
We propose a directed graphical representation of utility functions, called UCP-networks, that combines aspects of two existing preference models: generalized additive models and ...
We consider the problem of maximizing the reliability of a series-parallel system given cost and weight constraints on the system. The number of components in each subsystem and th...
— We consider the general problem of optimizing the performance of OBS networks with multiple traffic classes subject to strict (absolute) QoS constraints in terms of the end-to...