198 citations · 214 across the 7 of their papers we have counts for
7 papers
Distributionally Robust Semi-Supervised Learning Over Graphs
Alireza Sadeghi, Meng Ma, Bingcong Li +1
Semi-supervised learning (SSL) over graph-structured data emerges in many network science applications. To efficiently manage learning over graphs, variants of graph neural network…
Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data
Alireza Sadeghi, Gang Wang, Meng Ma +1
Data used to train machine learning models can be adversarial--maliciously constructed by adversaries to fool the model. Challenge also arises by privacy, confidentiality, or due t…
On the Convergence of SARAH and Beyond
Bingcong Li, Meng Ma, Georgios B. Giannakis
The main theme of this work is a unifying algorithm, \textbf{L}oop\textbf{L}ess \textbf{S}ARAH (L2S) for problems formulated as summation of individual loss functions. L2S broa…
Tight Linear Convergence Rate of ADMM for Decentralized Optimization
Meng Ma, Bingcong Li, Georgios B. Giannakis
The present paper considers leveraging network topology information to improve the convergence rate of ADMM for decentralized optimization, where networked nodes work collaborative…
Fast Decentralized Optimization over Networks
Meng Ma, Athanasios N. Nikolakopoulos, Georgios B. Giannakis
The present work introduces the hybrid consensus alternating direction method of multipliers (H-CADMM), a novel framework for optimization over networks which unifies existing dist…
Kernel-based Inference of Functions over Graphs
Vassilis N. Ioannidis, Meng Ma, Athanasios N. Nikolakopoulos +2
The study of networks has witnessed an explosive growth over the past decades with several ground-breaking methods introduced. A particularly interesting -- and prevalent in severa…
Kernel-based Reconstruction of Graph Signals
Daniel Romero, Meng Ma, Georgios B. Giannakis
A number of applications in engineering, social sciences, physics, and biology involve inference over networks. In this context, graph signals are widely encountered as descriptors…