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Found 3 papers, 1 papers with code
Federated Linear Contextual Bandits with Heterogeneous Clients
The demand for collaborative and private bandit learning across multiple agents is surging due to the growing quantity of data generated from distributed systems.
Graph Structural Attack by Perturbing Spectral Distance
Graph Convolutional Networks (GCNs) have fueled a surge of research interest due to their encouraging performance on graph learning tasks, but they are also shown vulnerability to adversarial attacks.
Graph Embedding with Hierarchical Attentive Membership
The exploitation of graph structures is the key to effectively learning representations of nodes that preserve useful information in graphs.
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