Collective control of a multiagent system is concerned with designing strategies for a group
of autonomous agents operating in a networked environment. The aim is to achieve a global …

Estimation of functions from sparse and noisy data is a central theme in machine learning. In
the last few years, many algorithms have been developed that exploit Tikhonov …

The problem of identifying dynamical models on the basis of measurement data is usually
considered in a classical open-loop or closed-loop setting. In this paper, this problem is …

We review the state of the art of clustering financial time series and the study of their
correlations alongside other interaction networks. The aim of the review is to gather in one …

Determining interrelatedness structure of various entities from multiple time series data is of
significant interest to many areas. Knowledge of such a structure can aid in identifying cause …

Modeling and identification of high dimensional systems, involving signals with many
components, poses severe challenges to off-the-shelf techniques for system identification …

We propose a graphical model for representing networks of stochastic processes, the
minimal generative model graph. It is based on reduced factorizations of the joint distribution …

System identification learns mathematical models of dynamic systems starting from input–
output data. Despite its long history, such research area is still extremely active. New …

This paper addresses the problem of obtaining an estimate of a particular module of interest
that is embedded in a dynamic network with known interconnection structure. In this paper it …

Much recent research has dealt with the identifiability of a dynamical network in which the
node signals are connected by causal linear transfer functions and are excited by known …