In this paper, we present an AUC (i.e., the Area Under the Curve of Receiver Operating Characteristics (ROC)) maximization based learning algorithm to design the classifier for ma...
We extend the well-known BFGS quasi-Newton method and its memory-limited variant LBFGS to the optimization of nonsmooth convex objectives. This is done in a rigorous fashion by ge...
Policy gradient (PG) reinforcement learning algorithms have strong (local) convergence guarantees, but their learning performance is typically limited by a large variance in the e...
Abstract. Motivated by the need for agent classification in sensor networking and autonomous vehicle control applications, we propose a flexible and distributed stochastic automato...
In recent years, due to the increasing popularization of data broadcasting, the volume and variety of data being broadcast are rapidly increasing. In this environment, as it is di...