Zhengchao Wan

Orcid: 0000-0003-4388-6991

According to our database1, Zhengchao Wan authored at least 16 papers between 2019 and 2024.

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Bibliography

2024
Random Walks, Conductance, and Resistance for the Connection Graph Laplacian.
SIAM J. Matrix Anal. Appl., 2024

All You Need is Resistance: On the Equivalence of Effective Resistance and Certain Optimal Transport Problems on Graphs.
CoRR, 2024

Comparing Graph Transformers via Positional Encodings.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Distances for Markov Chains, and Their Differentiation.
Proceedings of the International Conference on Algorithmic Learning Theory, 2024

2023
The Ultrametric Gromov-Wasserstein Distance.
Discret. Comput. Geom., December, 2023

The Gromov-Hausdor? distance between ultrametric spaces: its structure and computation.
J. Comput. Geom., 2023

The Weisfeiler-Lehman Distance: Reinterpretation and Connection with GNNs.
Proceedings of the Topological, 2023

The Numerical Stability of Hyperbolic Representation Learning.
Proceedings of the International Conference on Machine Learning, 2023

The Persistent Laplacian for Data Science: Evaluating Higher-Order Persistent Spectral Representations of Data.
Proceedings of the International Conference on Machine Learning, 2023

Understanding Oversquashing in GNNs through the Lens of Effective Resistance.
Proceedings of the International Conference on Machine Learning, 2023

A Generalization of the Persistent Laplacian to Simplicial Maps.
Proceedings of the 39th International Symposium on Computational Geometry, 2023

2022
Persistent Laplacians: Properties, Algorithms and Implications.
SIAM J. Math. Data Sci., June, 2022

Weisfeiler-Lehman Meets Gromov-Wasserstein.
Proceedings of the International Conference on Machine Learning, 2022

2021
The Gromov-Hausdorff distance between ultrametric spaces: its structure and computation.
CoRR, 2021

2020
The Gaussian Transform.
CoRR, 2020

2019
The Wasserstein Transform.
Proceedings of the 36th International Conference on Machine Learning, 2019


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