On dyadic fairness: Exploring and mitigating bias in graph connections P Li, Y Wang, H Zhao, P Hong, H Liu International Conference on Learning Representations, 2021 | 142 | 2021 |
Motif-based graph representation learning with application to chemical molecules Y Wang, S Chen, G Chen, E Shurberg, H Liu, P Hong Informatics 10 (1), 8, 2023 | 11 | 2023 |
Asymmetric contrastive multimodal learning for advancing chemical understanding H Xu, Y Wang, Y Li, P Hong arXiv preprint arXiv:2311.06456, 2023 | 4 | 2023 |
A machine learning approach to robustly determine director fields and analyze defects in active nematics Y Li, Z Zarei, PN Tran, Y Wang, A Baskaran, S Fraden, MF Hagan, P Hong Soft matter 20 (8), 1869-1883, 2024 | 3 | 2024 |
Knowledgebra: An Algebraic Learning Framework for Knowledge Graph T Yang, Y Wang, L Sha, J Engelbrecht, P Hong Machine Learning and Knowledge Extraction 4, 432--445, 2022 | 3 | 2022 |
Generative large language models in electronic health records for patient care since 2023: a systematic review X Du, Z Zhou, Y Wang, YW Chuang, R Yang, W Zhang, X Wang, R Zhang, ... medRxiv, 2024 | 2 | 2024 |
Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases Y Wang, L Wang, Z Zhou, J Laurentiev, JR Lakin, L Zhou, P Hong Journal of biomedical informatics 156, 104677, 2024 | 2 | 2024 |
Counterpart Fairness--Addressing Systematic between-group Differences in Fairness Evaluation Y Wang, Z Zhou, L Wang, J Laurentiev, P Hou, L Zhou, P Hong arXiv preprint arXiv:2305.18160, 2023 | | 2023 |
State-level COVID-19 Trend Forecasting Using Mobility and Policy Data Y Wang, H Peng, L Sha, Z Liu, P Hong medRxiv, 2021.01. 04.21249218, 2021 | | 2021 |