Adaptive networks
This paper surveys recent advances related to adaptation, learning, and optimization over
networks. Various distributed strategies are discussed that enable a collection of networked …
networks. Various distributed strategies are discussed that enable a collection of networked …
Adaptation, learning, and optimization over networks
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 …
largely self-contained and covers results that relate to the analysis and design of multi-agent …
Diffusion adaptation over networks
Adaptive networks are well-suited to perform decentralized information processing and
optimization tasks and to model various types of self-organized and complex behavior …
optimization tasks and to model various types of self-organized and complex behavior …
Adaptive learning in a world of projections
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 …
both for classification and regression tasks, in a timeadaptive setting and in (infinite …
Online Sparse System Identification and Signal Reconstruction Using Projections Onto Weighted Balls
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 …
system identification. The sequentially observed data are used to generate an equivalent …
Sparsity-aware data-selective adaptive filters
We propose two adaptive filtering algorithms that combine sparsity-promoting schemes with
data-selection mechanisms. Sparsity is promoted via some well-known nonconvex …
data-selection mechanisms. Sparsity is promoted via some well-known nonconvex …
Adversarial robustness for tabular data through cost and utility awareness
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 …
data in tabular domains. Adversarial examples can be particularly damaging for these …
Online dictionary learning for kernel LMS
Adaptive filtering algorithms operating in reproducing kernel Hilbert spaces have
demonstrated superiority over their linear counterpart for nonlinear system identification …
demonstrated superiority over their linear counterpart for nonlinear system identification …
DCD-RLS adaptive filters with penalties for sparse identification
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 …
dichotomous coordinate descent (DCD) iterations for identification of sparse systems. The …
Sparsity regularised recursive least squares adaptive filtering
The authors propose a new approach for the adaptive identification of sparse systems. This
approach improves on the recursive least squares (RLS) algorithm by adding a sparsity …
approach improves on the recursive least squares (RLS) algorithm by adding a sparsity …