Abstract: High-dimensional time series appear in many scientific setups, demanding a nuanced approach to model and analyze the underlying dependence structure. Theoretical advancements so far often ...

Abstract: With the explosive growth of graph data in scale, noise, and structural complexity, existing graph neural networks (GNNs) are reaching a performance bottleneck when simultaneously modelling ...