The majority of the work in the area of Markov decision processes has focused on expected values of rewards in the objective function and expected costs in the constraints. Althou...
Although tabular reinforcement learning (RL) methods have been proved to converge to an optimal policy, the combination of particular conventional reinforcement learning techniques...
Abstract. This paper describes a novel approach to recovering a parametric deformation that optimally registers one image to another. The method proceeds by constructing a global c...
Abstract. The Mumford-Shah model has been one of the most powerful models in image segmentation and denoising. The optimization of the multiphase Mumford-Shah energy functional has...
We consider random approximations to deterministic optimization problems. The objective function and the constraint set can be approximated simultaneously. Relying on concentratio...