Can they do this solely through interactions with each other? We focus this question on the ability of agents to attain the value of their Stackelberg optimal strategy and study the impact of information asymmetry.

Theconstructed result empirically captures theprecise dynamic correlation ofsixsynthetic MTS datasets generated by a non-repeating random walk model.

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In recent years, graph neural networks (GNNs) have become the de facto tool for performing machine learning tasks on graphs. Most GNNs belong to the family of message passing neural networks (MPNNs).

The data-driven nature of modern machine learning (ML) training routines puts pressure on data supply pipelines, which become increasingly more complex. It is common to find separate disks or whole content distribution networks dedicated to servicing massive datasets. Training is often distributed across multiple workers. This emergent complexity gives a perfect opportunity for an attackertodisrupt ML training, while remaining covert.

The labeling scarcity is even amplified when we come to adversarially robust deep learning [9], i.e., to training deep models that are not fooled by maliciously crafted, although imperceivable perturbations.

This increase in data sources has led to applications that are increasingly high-dimensional. To be both statistically and computationally efficient in this setting, it is then important to develop approaches that can exploit the structure within the data.