We apply CMA-ES, an evolution strategy with covariance matrix adaptation, and TDL (Temporal Difference Learning) to reinforcement learning tasks. In both cases these algorithms se...
We investigate the effects of precision on the efficiency of various local search algorithms on 1-D unimodal functions. We present a (1 + 1)-EA with adaptive step size which fin...
Martin Dietzfelbinger, Jonathan E. Rowe, Ingo Wege...
— We address the problem of energy efficient sensing by adaptively coordinating the sleep schedules of sensor nodes while guaranteeing that values of sleeping nodes can be recov...
A central challenge in ad hoc networks is the design of routing protocols that can adapt their behavior to frequent and rapid changes in the network. The performance of proactive ...
Venugopalan Ramasubramanian, Zygmunt J. Haas, Emin...
In this paper, we present an information-theoretic approach to learning a Mahalanobis distance function. We formulate the problem as that of minimizing the differential relative e...
Jason V. Davis, Brian Kulis, Prateek Jain, Suvrit ...