Related references
Note: Only part of the references are listed.An Efficient Framework for Clustered Federated Learning
Avishek Ghosh et al.
IEEE TRANSACTIONS ON INFORMATION THEORY (2022)
Wearable devices for the detection of COVID-19
H. Ceren Ates et al.
NATURE ELECTRONICS (2021)
Network Modeling and Analysis of COVID-19 Testing Strategies
Siqi Zhang et al.
2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) (2021)
FEDERATED LEARNING FROM BIG DATA OVER NETWORKS
Y. Sarcheshmehpour et al.
2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021) (2021)
Local Graph Clustering With Network Lasso
Alexander Jung et al.
IEEE SIGNAL PROCESSING LETTERS (2021)
Inexact first-order primal-dual algorithms
Julian Rasch et al.
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS (2020)
Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning
Roula Nassif et al.
IEEE SIGNAL PROCESSING MAGAZINE (2020)
A network-based explanation of why most COVID-19 infection curves are linear
Stefan Thurner et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2020)
The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology
Kyra H. Grantz et al.
NATURE COMMUNICATIONS (2020)
Federated learning for privacy-preserving AI
Yong Cheng et al.
COMMUNICATIONS OF THE ACM (2020)
Cost-efficient Distributed Optimization In Machine Learning Over Wireless Networks
Afsaneh Mahmoudi et al.
ICC 2020 - 2020 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC) (2020)
Data Analytics on Graphs Part III: Machine Learning on Graphs, from Graph Topology to Applications
Ljubisa Stankovic et al.
FOUNDATIONS AND TRENDS IN MACHINE LEARNING (2020)
On the Duality Between Network Flows and Network Lasso
Alexander Jung
IEEE SIGNAL PROCESSING LETTERS (2020)
Vector-Valued Graph Trend Filtering With Non-Convex Penalties
Rohan Varma et al.
IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS (2020)
Localized Linear Regression in Networked Data
Alexander Jung et al.
IEEE SIGNAL PROCESSING LETTERS (2019)
Semi-Supervised Learning in Network-Structured Data via Total Variation Minimization
Alexander Jung et al.
IEEE TRANSACTIONS ON SIGNAL PROCESSING (2019)
The Future of Industrial Communication
Martin Wollschlaeger et al.
IEEE INDUSTRIAL ELECTRONICS MAGAZINE (2017)
An introduction to continuous optimization for imaging
Antonin Chambolle et al.
ACTA NUMERICA (2016)
Network Lasso: Clustering and Optimization in Large Graphs
David Hallac et al.
KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING (2015)
Learning Mixtures of Gaussians in High Dimensions
Rong Ge et al.
STOC'15: PROCEEDINGS OF THE 2015 ACM SYMPOSIUM ON THEORY OF COMPUTING (2015)
On the Convergence of Primal-Dual Hybrid Gradient Algorithm
Bingsheng He et al.
SIAM JOURNAL ON IMAGING SCIENCES (2014)
A Primal-Dual Splitting Method for Convex Optimization Involving Lipschitzian, Proximable and Linear Composite Terms
Laurent Condat
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS (2013)
Message-Passing Algorithms for Inference and Optimization Belief Propagation and Divide and Concur
Jonathan S. Yedidia
JOURNAL OF STATISTICAL PHYSICS (2011)
Network medicine: a network-based approach to human disease
Albert-Laszlo Barabasi et al.
NATURE REVIEWS GENETICS (2011)
Distributed Subgradient Methods for Multi-Agent Optimization
Angelia Nedic et al.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2009)
Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using l1-Constrained Quadratic Programming (Lasso)
Martin J. Wainwright
IEEE TRANSACTIONS ON INFORMATION THEORY (2009)
An elementary proof of the triangle inequality for the Wasserstein metric
Philippe Clement et al.
PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY (2008)
A tutorial on spectral clustering
Ulrike von Luxburg
STATISTICS AND COMPUTING (2007)