This paper surveys recent advances related to adaptation, learning, and optimization over
networks. Various distributed strategies are discussed that enable a collection of networked …

This work deals with the topic of information processing over graphs. The presentation is
largely self-contained and covers results that relate to the analysis and design of multi-agent …

Adaptive networks are well-suited to perform decentralized information processing and
optimization tasks and to model various types of self-organized and complex behavior …

This article presents a general tool for convexly constrained parameter/function estimation
both for classification and regression tasks, in a timeadaptive setting and in (infinite …

This paper presents a novel projection-based adaptive algorithm for sparse signal and
system identification. The sequentially observed data are used to generate an equivalent …

We propose two adaptive filtering algorithms that combine sparsity-promoting schemes with
data-selection mechanisms. Sparsity is promoted via some well-known nonconvex …

Adaptive filtering algorithms operating in reproducing kernel Hilbert spaces have
demonstrated superiority over their linear counterpart for nonlinear system identification …

Many safety-critical applications of machine learning, such as fraud or abuse detection, use
data in tabular domains. Adversarial examples can be particularly damaging for these …

In this paper, we propose a family of low-complexity adaptive filtering algorithms based on
dichotomous coordinate descent (DCD) iterations for identification of sparse systems. The …

We propose two versions of affine projection (AP) algorithms tailored for sparse system
identification (SSI). Contrary to most adaptive filtering algorithms devised for SSI, which are …