A Short Tutorial on The Weisfeiler-Lehman Test And Its Variants. - Ningyuan Teresa Huang; Soledad Villar (ICASSP, 2021)

Prospective Learning: Back to the Future. - Joshua T. Vogelstein; Timothy D. Verstynen; Konrad P. Kording; Leyla Isik; John W. Krakauer; Ralph Etienne-Cummings; Elizabeth L. Ogburn; Carey E. Priebe; Randal C. Burns; Kwame S. Kutten; James J. Knierim; James B. Potash; et al. (CoRR, 2022)

Deep Learning with Label Noise: A Hierarchical Approach. - Li Chen; Ningyuan Huang; Cong Mu; Hayden S. Helm; Kate Lytvynets; Weiwei Yang 004; Carey E. Priebe (CoRR, 2022)

Endowing Language Models with Multimodal Knowledge Graph Representations. - Ningyuan Huang; Yash R. Deshpande; Yibo Liu; Houda Alberts; Kyunghyun Cho; Clara Vania; Iacer Calixto (CoRR, 2022)

From Local to Global: Spectral-Inspired Graph Neural Networks. - Ningyuan Huang; Soledad Villar; Carey E. Priebe; Da Zheng; Chengyue Huang; Lin Yang 011; Vladimir Braverman (CoRR, 2022)

Deep Learning is Provably Robust to Symmetric Label Noise. - Carey E. Priebe; Ningyuan Huang; Soledad Villar; Cong Mu; Li Chen (CoRR, 2022)

Graph Neural Networks for Community Detection on Sparse Graphs. - Luana Ruiz; Ningyuan Huang; Soledad Villar (CoRR, 2022)

A Simple Spectral Failure Mode for Graph Convolutional Networks. - Carey E. Priebe; Cencheng Shen; Ningyuan Huang; Tianyi Chen (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Dimensionality Reduction, Regularization, and Generalization in Overparameterized Regressions. - Ningyuan Teresa Huang; David W. Hogg; Soledad Villar (SIAM J. Math. Data Sci., 2022)

Prospective Learning: Principled Extrapolation to the Future. - Ashwin De Silva; Rahul Ramesh; Lyle H. Ungar; Marshall G. Hussain Shuler; Noah J. Cowan; Michael L. Platt; Chen Li; Leyla Isik; Seung-Eon Roh; Adam S. Charles; Archana Venkataraman; Brian Caffo; et al. (CoLLAs, 2023)

Fine-grained Expressivity of Graph Neural Networks. - Jan Böker; Ron Levie; Ningyuan Huang; Soledad Villar; Christopher Morris 001 (NeurIPS, 2023)

Approximately Equivariant Graph Networks. - Ningyuan Huang; Ron Levie; Soledad Villar (NeurIPS, 2023)

A Spectral Analysis of Graph Neural Networks on Dense and Sparse Graphs. - Luana Ruiz; Ningyuan Teresa Huang; Soledad Villar (ICASSP, 2024)

Learning functions on symmetric matrices and point clouds via lightweight invariant features. - Ben Blum-Smith; Ningyuan Huang; Marco Cuturi; Soledad Villar (CoRR, 2024)

An EEG abnormality detection algorithm based on graphic attention network. - Junwei Duan; Fei Xie 007; Ningyuan Huang; Ningdi Luo; Ziyu Guan; Wei Zhao 019; Gang Gao (Multim. Tools Appl., 2024)

CAO-RONet: A Robust 4D Radar Odometry with Exploring More Information from Low-Quality Points. - Zhiheng Li 003; Yubo Cui; Ningyuan Huang; Chenglin Pang; Zheng Fang 001 (CoRR, 2025)

Understanding Input Selectivity in Mamba: Impact on Approximation Power, Memorization, and Associative Recall Capacity. - Ningyuan Huang; Miguel Sarabia; Abhinav Moudgil; Pau Rodríguez; Luca Zappella; Federico Danieli (CoRR, 2025)

CosmoBench: A Multiscale, Multiview, Multitask Cosmology Benchmark for Geometric Deep Learning. - Ningyuan Huang; Richard Stiskalek; Jun-Young Lee; Adrian E. Bayer; Charles C. Margossian; Christian Kragh Jespersen; Lucia A. Perez; Lawrence K. Saul; Francisco Villaescusa-Navarro (CoRR, 2025)