In this paper, we propose a versatile graph inference framework for learning from graph signals corrupted by exponential family noise. Our framework generalizes previous methods from continuous smooth ...

The rapidly growing field of artificial intelligence (AI) has the potential to revolutionise our lives. With AI already powering everything from self-d ...

Lipschitz extensions were proposed as a tool for designing differentially private algorithms for approximating graph statistics. However, efficiently computable Lipschitz extensions were known only ...

Examine the set of graphs below for a given city. Read carefully the temperature and precipitation scales on the graphs. Review the biome information. Two biome choices are given for each set of ...

This is a comprehensive repository that brings together our work on deep temporal graph clustering, including a series of related papers, open source datasets, and implementations of the TGC base code ...

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