Dillon Ze Chen — accepted AI-conference papers

2024

Graph Learning for Numeric Planning

NeurIPS 2024poster

Graph learning is naturally well suited for use in symbolic, object-centric planning due to its ability to exploit relational structures exhibited in planning domains and to take as input planning instances with arbitrary number of objects. Numeric planning is an extension of symbolic planning in wh…

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2023

$\mathscr{N}$-WL: A New Hierarchy of Expressivity for Graph Neural Networks

Qing Wang, Dillon Ze Chen, Asiri Wijesinghe, Shouheng Li, Muhammad Farhan

ICLR 2023poster

The expressive power of Graph Neural Networks (GNNs) is fundamental for understanding their capabilities and limitations, i.e., what graph properties can or cannot be learnt by a GNN. Since standard GNNs have been characterised to be upper-bounded by the Weisfeiler-Lehman (1-WL) algorithm, recent a…

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