  Sensor scheduling is a well studied problem in signal processing and control
with numerous applications. Despite its successful history, most of the related
literature assumes the knowledge of the underlying probabilistic model of the
sensor measurements such as the correlation structure or the entire joint
probability density function. Herein, a framework for sensor scheduling for
remote estimation is introduced in which the system design and the scheduling
decisions are based solely on observed data. Unicast and broadcast networks and
corresponding receivers are considered. In both cases, the empirical risk
minimization can be posed as a difference-of-convex optimization problem and
locally optimal solutions are obtained efficiently by applying the
convex-concave procedure. Our results are independent of the data's probability
density function, correlation structure and the number of sensors.
