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04/25/2023
Model Extraction Attacks Against Reinforcement Learning Based Controllers
We introduce the problem of model-extraction attacks in cyber-physical s...
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03/26/2023
FairGAT: Fairness-aware Graph Attention Networks
Graphs can facilitate modeling various complex systems such as gene netw...
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03/20/2023
Fairness-Aware Graph Filter Design
Graphs are mathematical tools that can be used to represent complex real...
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07/22/2022
Explaining Dynamic Graph Neural Networks via Relevance Back-propagation
Graph Neural Networks (GNNs) have shown remarkable effectiveness in capt...
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05/20/2022
FairNorm: Fair and Fast Graph Neural Network Training
Graph neural networks (GNNs) have been demonstrated to achieve state-of-...
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02/27/2022
Graph-Assisted Communication-Efficient Ensemble Federated Learning
Communication efficiency arises as a necessity in federated learning due...
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01/21/2022
Fair Node Representation Learning via Adaptive Data Augmentation
Node representation learning has demonstrated its efficacy for various a...
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07/02/2021
Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network
This paper considers predicting future statuses of multiple agents in an...
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06/15/2021
Online Learning with Uncertain Feedback Graphs
Online learning with expert advice is widely used in various machine lea...
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06/09/2021
Fairness-Aware Node Representation Learning
Node representation learning has demonstrated its effectiveness for vari...
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06/09/2021
Multiple Kernel Representation Learning on Networks
Learning representations of nodes in a low dimensional space is a crucia...
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02/09/2021
Graph-Aided Online Multi-Kernel Learning
Multi-kernel learning (MKL) has been widely used in function approximati...
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12/03/2018
Online Graph-Adaptive Learning with Scalability and Privacy
Graphs are widely adopted for modeling complex systems, including financ...
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05/16/2018
Semi-Blind Inference of Topologies and Dynamical Processes over Graphs
Network science provides valuable insights across numerous disciplines i...
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03/27/2018
Canonical Correlation Analysis of Datasets with a Common Source Graph
Canonical correlation analysis (CCA) is a powerful technique for discove...
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01/29/2018
Nonlinear Dimensionality Reduction on Graphs
In this era of data deluge, many signal processing and machine learning ...
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12/28/2017
Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics
Kernel-based methods exhibit well-documented performance in various nonl...
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12/28/2017
Online Ensemble Multi-kernel Learning Adaptive to Non-stationary and Adversarial Environments
Kernel-based methods exhibit well-documented performance in various nonl...
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10/26/2016
Tensor Decompositions for Identifying Directed Graph Topologies and Tracking Dynamic Networks
Directed networks are pervasive both in nature and engineered systems, o...
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10/20/2016
Nonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivity
Structural equation models (SEMs) and vector autoregressive models (VARM...
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09/27/2016
Online Categorical Subspace Learning for Sketching Big Data with Misses
With the scale of data growing every day, reducing the dimensionality (a...
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05/10/2016
