Reinforcement learning algorithms can become unstable when combined with linear function approximation. Algorithms that minimize the mean-square Bellman error are guaranteed to co...
Sparse linear solvers account for much of the execution time in many high-performance computing (HPC) applications, and not every solver works on all problems. Hence choosing a su...
In this paper we propose a very simple FIR pre-filter based method for near optimal least-squares linear approximation of discrete time signals. A digital pre-processing filter,...
Marco Dalai, Riccardo Leonardi, Pierangelo Miglior...
Recently, sample complexity bounds have been derived for problems involving linear functions such as neural networks and support vector machines. In many of these theoretical stud...
In defining large, complex access control policies, one would like to compose sub-policies, perhaps authored by different organizations, into a single global policy. Existing po...