In various applications, the effect of errors in gradient-based iterations is of particular importance when seeking saddle points of the Lagrangian function associated with constra...
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
— Existing sensor network architectures are based on the assumption that data will be polled. Therefore, they are not adequate for long-term battery-powered use in applications t...
Sasha Jevtic, Mathew Kotowsky, Robert P. Dick, Pet...
Wireless networks are a common place nowadays and almost all of the modern devices support wireless communication in some form. These networks differ from more traditional computi...
We study efficient importance sampling techniques for particle filtering (PF) when either (a) the observation likelihood (OL) is frequently multimodal or heavy-tailed, or (b) the s...