6 citations · 8 across the 6 of their papers we have counts for
6 papers
Graph neural networks extrapolate out-of-distribution for shortest paths
Robert R. Nerem, Samantha Chen, Sanjoy Dasgupta +1
Neural networks (NNs), despite their success and wide adoption, still struggle to extrapolate out-of-distribution (OOD), i.e., to inputs that are not well-represented by their trai…
Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions
Samantha Chen, Yusu Wang
Learning distance functions between complex objects, such as the Wasserstein distance to compare point sets, is a common goal in machine learning applications. However, functions o…
The Weisfeiler-Lehman Distance: Reinterpretation and Connection with GNNs
Samantha Chen, Sunhyuk Lim, Facundo Mémoli +2
In this paper, we present a novel interpretation of the so-called Weisfeiler-Lehman (WL) distance, introduced by Chen et al. (2022), using concepts from stochastic processes. The W…
Learning Ultrametric Trees for Optimal Transport Regression
Samantha Chen, Puoya Tabaghi, Yusu Wang
Optimal transport provides a metric which quantifies the dissimilarity between probability measures. For measures supported in discrete metric spaces, finding the optimal transport…
Weisfeiler-Lehman meets Gromov-Wasserstein
Samantha Chen, Sunhyuk Lim, Facundo Mémoli +2
The Weisfeiler-Lehman (WL) test is a classical procedure for graph isomorphism testing. The WL test has also been widely used both for designing graph kernels and for analyzing gra…
Approximation algorithms for 1-Wasserstein distance between persistence diagrams
Samantha Chen, Yusu Wang
Recent years have witnessed a tremendous growth using topological summaries, especially the persistence diagrams (encoding the so-called persistent homology) for analyzing complex…