Zhengchao Wan

According to our database, Zhengchao Wan authored at least 22 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2026

Two Calm Ends and the Wild Middle: A Geometric Picture of Memorization in Diffusion Models.

Nick Dodson, Xinyu Gao, Qingsong Wang, Yusu Wang, Zhengchao Wan

CoRR, February, 2026

2025

Robust Graph-Based Semi-Supervised Learning via <i>p</i>-Conductances.

Sawyer Jack Robertson, Chester Holtz, Zhengchao Wan, Gal Mishne, Alexander Cloninger

CoRR, February, 2025

Seeds of Structure: Patch PCA Reveals Universal Compositional Cues in Diffusion Models.

Qingsong Wang, Zhengchao Wan, Misha Belkin, Yusu Wang

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Elucidating Flow Matching ODE Dynamics via Data Geometry and Denoisers.

Zhengchao Wan, Qingsong Wang, Gal Mishne, Yusu Wang

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

Random Walks, Conductance, and Resistance for the Connection Graph Laplacian.

Alexander Cloninger, Gal Mishne, Andreas Oslandsbotn, Sawyer Jack Robertson, Zhengchao Wan, Yusu Wang

SIAM J. Matrix Anal. Appl., 2024

Elucidating Flow Matching ODE Dynamics with respect to Data Geometries.

Gal Mishne, Zhengchao Wan, Qingsong Wang, Yusu Wang

CoRR, 2024

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

Sawyer Jack Robertson, Zhengchao Wan, Alexander Cloninger

CoRR, 2024

Continuous Partitioning for Graph-Based Semi-Supervised Learning.

Chester Holtz, Pengwen Chen, Zhengchao Wan, Chung-Kuan Cheng, Gal Mishne

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Comparing Graph Transformers via Positional Encodings.

Mitchell Black, Zhengchao Wan, Gal Mishne, Amir Nayyeri, Yusu Wang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Distances for Markov Chains, and Their Differentiation.

Tristan Brugère, Zhengchao Wan, Yusu Wang

Proceedings of the International Conference on Algorithmic Learning Theory, 2024

2023

The Ultrametric Gromov-Wasserstein Distance.

Facundo Mémoli, Axel Munk, Zhengchao Wan, Christoph Weitkamp

Discret. Comput. Geom., December, 2023

The Gromov-Hausdor? distance between ultrametric spaces: its structure and computation.

Facundo Mémoli, Zane T. Smith, Zhengchao Wan

J. Comput. Geom., 2023

The Weisfeiler-Lehman Distance: Reinterpretation and Connection with GNNs.

Samantha Chen, Sunhyuk Lim, Facundo Mémoli, Zhengchao Wan, Yusu Wang

Proceedings of the Topological, 2023

The Numerical Stability of Hyperbolic Representation Learning.

Gal Mishne, Zhengchao Wan, Yusu Wang, Sheng Yang

Proceedings of the International Conference on Machine Learning, 2023

The Persistent Laplacian for Data Science: Evaluating Higher-Order Persistent Spectral Representations of Data.

Thomas Davies, Zhengchao Wan, Rubén J. Sánchez-García

Proceedings of the International Conference on Machine Learning, 2023

Understanding Oversquashing in GNNs through the Lens of Effective Resistance.

Mitchell Black, Zhengchao Wan, Amir Nayyeri, Yusu Wang

Proceedings of the International Conference on Machine Learning, 2023

A Generalization of the Persistent Laplacian to Simplicial Maps.

Aziz Burak Gülen, Facundo Mémoli, Zhengchao Wan, Yusu Wang

Proceedings of the 39th International Symposium on Computational Geometry, 2023

2022

Persistent Laplacians: Properties, Algorithms and Implications.

Facundo Mémoli, Zhengchao Wan, Yusu Wang

SIAM J. Math. Data Sci., June, 2022

Weisfeiler-Lehman Meets Gromov-Wasserstein.

Samantha Chen, Sunhyuk Lim, Facundo Mémoli, Zhengchao Wan, Yusu Wang

Proceedings of the International Conference on Machine Learning, 2022

2021

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

Facundo Mémoli, Zane T. Smith, Zhengchao Wan

CoRR, 2021

2020

The Gaussian Transform.

Kun Jin, Facundo Mémoli, Zhengchao Wan

CoRR, 2020

2019

The Wasserstein Transform.

Facundo Mémoli, Zane T. Smith, Zhengchao Wan

Proceedings of the 36th International Conference on Machine Learning, 2019