98 citations · 527 across the 30 of their papers we have counts for
30 papers
Topology Identification and Inference over Graphs
Gonzalo Mateos, Yanning Shen, Georgios B. Giannakis +1
Topology identification and inference of processes evolving over graphs arise in timely applications involving brain, transportation, financial, power, as well as social and inform…
Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities
Jarvis Haupt, Qin Lu, Yanning Shen +5
Powerful artificial intelligence (AI) tools that have emerged in recent years -- including large language models, automated coding assistants, and advanced image and speech generat…
Personalized Online Federated Learning with Multiple Kernels
Pouya M. Ghari, Yanning Shen
Multi-kernel learning (MKL) exhibits well-documented performance in online non-linear function approximation. Federated learning enables a group of learners (called clients) to tra…
Model Extraction Attacks Against Reinforcement Learning Based Controllers
Momina Sajid, Yanning Shen, Yasser Shoukry
We introduce the problem of model-extraction attacks in cyber-physical systems in which an attacker attempts to estimate (or extract) the feedback controller of the system. Extract…
FairGAT: Fairness-aware Graph Attention Networks
O. Deniz Kose, Yanning Shen
Graphs can facilitate modeling various complex systems such as gene networks and power grids, as well as analyzing the underlying relations within them. Learning over graphs has re…
Fairness-Aware Graph Filter Design
O. Deniz Kose, Yanning Shen, Gonzalo Mateos
Graphs are mathematical tools that can be used to represent complex real-world systems, such as financial markets and social networks. Hence, machine learning (ML) over graphs has…
Change Point Detection Approach for Online Control of Unknown Time Varying Dynamical Systems
Deepan Muthirayan, Ruijie Du, Yanning Shen +1
We propose a novel change point detection approach for online learning control with full information feedback (state, disturbance, and cost feedback) for unknown time-varying dynam…
Explaining Dynamic Graph Neural Networks via Relevance Back-propagation
Jiaxuan Xie, Yezi Liu, Yanning Shen
Graph Neural Networks (GNNs) have shown remarkable effectiveness in capturing abundant information in graph-structured data. However, the black-box nature of GNNs hinders users fro…
FairNorm: Fair and Fast Graph Neural Network Training
O. Deniz Kose, Yanning Shen
Graph neural networks (GNNs) have been demonstrated to achieve state-of-the-art for a number of graph-based learning tasks, which leads to a rise in their employment in various dom…
Graph-Assisted Communication-Efficient Ensemble Federated Learning
Pouya M Ghari, Yanning Shen
Communication efficiency arises as a necessity in federated learning due to limited communication bandwidth. To this end, the present paper develops an algorithmic framework where…
Fair Node Representation Learning via Adaptive Data Augmentation
O. Deniz Kose, Yanning Shen
Node representation learning has demonstrated its efficacy for various applications on graphs, which leads to increasing attention towards the area. However, fairness is a largely…
Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network
Maosen Li, Siheng Chen, Yanning Shen +3
This paper considers predicting future statuses of multiple agents in an online fashion by exploiting dynamic interactions in the system. We propose a novel collaborative predictio…
Online Learning with Uncertain Feedback Graphs
Pouya M Ghari, Yanning Shen
Online learning with expert advice is widely used in various machine learning tasks. It considers the problem where a learner chooses one from a set of experts to take advice and m…
Fairness-Aware Node Representation Learning
Öykü Deniz Köse, Yanning Shen
Node representation learning has demonstrated its effectiveness for various applications on graphs. Particularly, recent developments in contrastive learning have led to promising…
Multiple Kernel Representation Learning on Networks
Abdulkadir Celikkanat, Yanning Shen, Fragkiskos D. Malliaros
Learning representations of nodes in a low dimensional space is a crucial task with numerous interesting applications in network analysis, including link prediction, node classific…
Graph-Aided Online Multi-Kernel Learning
Pouya M Ghari, Yanning Shen
Multi-kernel learning (MKL) has been widely used in function approximation tasks. The key problem of MKL is to combine kernels in a prescribed dictionary. Inclusion of irrelevant k…
Online Graph-Adaptive Learning with Scalability and Privacy
Yanning Shen, Geert Leus, Georgios B. Giannakis
Graphs are widely adopted for modeling complex systems, including financial, biological, and social networks. Nodes in networks usually entail attributes, such as the age or gender…
Semi-Blind Inference of Topologies and Dynamical Processes over Graphs
Vassilis N. Ioannidis, Yanning Shen, Georgios B. Giannakis
Network science provides valuable insights across numerous disciplines including sociology, biology, neuroscience and engineering. A task of major practical importance in these app…
Canonical Correlation Analysis of Datasets with a Common Source Graph
Jia Chen, Gang Wang, Yanning Shen +1
Canonical correlation analysis (CCA) is a powerful technique for discovering whether or not hidden sources are commonly present in two (or more) datasets. Its well-appreciated meri…
Nonlinear Dimensionality Reduction on Graphs
Yanning Shen, Panagiotis A. Traganitis, Georgios B. Giannakis
In this era of data deluge, many signal processing and machine learning tasks are faced with high-dimensional datasets, including images, videos, as well as time series generated f…
Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics
Yanning Shen, Tianyi Chen, Georgios B. Giannakis
Kernel-based methods exhibit well-documented performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific…
Tensor Decompositions for Identifying Directed Graph Topologies and Tracking Dynamic Networks
Yanning Shen, Brian Baingana, Georgios B. Giannakis
Directed networks are pervasive both in nature and engineered systems, often underlying the complex behavior observed in biological systems, microblogs and social interactions over…
Nonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivity
Yanning Shen, Brian Baingana, Georgios B. Giannakis
Structural equation models (SEMs) and vector autoregressive models (VARMs) are two broad families of approaches that have been shown useful in effective brain connectivity studies.…
Online Categorical Subspace Learning for Sketching Big Data with Misses
Yanning Shen, Morteza Mardani, Georgios B. Giannakis
With the scale of data growing every day, reducing the dimensionality (a.k.a. sketching) of high-dimensional data has emerged as a task of paramount importance. Relevant issues to…