54 citations · 105 across the 33 of their papers we have counts for
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cs.LG2022★ 3 cited
Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks
Anders Aamand, Justin Y. Chen, Piotr Indyk +5
Recent work shows that the expressive power of Graph Neural Networks (GNNs) in distinguishing non-isomorphic graphs is exactly the same as that of the Weisfeiler-Lehman (WL) graph…
cs.LG2022
Testing distributional assumptions of learning algorithms
Ronitt Rubinfeld, Arsen Vasilyan
There are many high dimensional function classes that have fast agnostic learning algorithms when assumptions on the distribution of examples can be made, such as Gaussianity or un…