We extend stochastic network optimization theory to treat networks with arbitrary sample paths for arrivals, channels, and mobility. The network can experience unexpected link or n...
Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (th...
We introduce a class of learning problems where the agent is presented with a series of tasks. Intuitively, if there is relation among those tasks, then the information gained duri...
Prefix hijacking has always been a big concern in the Internet. Some events made it into the international world-news, but most of them remain unreported or even unnoticed. The s...
We introduce an efficient method for synthesizing acceleration noise – sound produced when an object experiences abrupt rigidbody acceleration due to collisions or other contac...