| Latent adversarial training of graph convolution networks H Jin, X Zhang ICML workshop on learning and reasoning with graph-structured representations 2, 2019 | 60 | 2019 |
| Certified robustness of graph convolution networks for graph classification under topological attacks H Jin, Z Shi, VJSA Peruri, X Zhang Advances in neural information processing systems 33, 8463-8474, 2020 | 53 | 2020 |
| Robust Training of Graph Convolutional Networks via Latent Perturbation H Jin, X Zhang European Conference on Machine Learning (ECML), 2020 | 11 | 2020 |
| Gromov-Wasserstein Discrepancy with Local Differential Privacy for Distributed Structural Graphs H Jin, X Chen Thirty-First International Joint Conference on Artificial Intelligence …, 2022 | 9 | 2022 |
| Simulating aggregation algorithms for empirical verification of resilient and adaptive federated learning H Jin, N Yan, M Mortazavi 2020 IEEE/ACM International Conference on Big Data Computing, Applications …, 2020 | 9 | 2020 |
| Graph neural networks for detecting anomalies in scientific workflows H Jin, K Raghavan, G Papadimitriou, C Wang, A Mandal, M Kiran, ... The International Journal of High Performance Computing Applications 37 (3-4 …, 2023 | 7 | 2023 |
| Workflow anomaly detection with graph neural networks H Jin, K Raghavan, G Papadimitriou, C Wang, A Mandal, P Krawczuk, ... 2022 IEEE/ACM Workshop on Workflows in Support of Large-Scale Science (WORKS …, 2022 | 6 | 2022 |
| Certifying Robust Graph Classification under Orthogonal Gromov-Wasserstein Threats H Jin, Z Yu, X Zhang Advances in Neural Information Processing Systems, 2022 | 5 | 2022 |
| Flow-Bench: A dataset for computational workflow anomaly detection G Papadimitriou, H Jin, C Wang, R Mayani, K Raghavan, A Mandal, ... arXiv preprint arXiv:2306.09930, 2023 | 4 | 2023 |
| Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound H Jin, Z Yu, X Zhang The 38th Conference on Uncertainty in Artificial Intelligence, 2022 | 4 | 2022 |
| Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context Learning H Jin, G Papadimitriou, K Raghavan, P Zuk, P Balaprakash, C Wang, ... The International Conference for High Performance Computing, Networking …, 2024 | 2 | 2024 |
| Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement H Jin, P Balaprakash, A Zou, P Ghysels, AS Krishnapriyan, A Mate, ... Electric Power Systems Research 235, 110795, 2024 | 2 | 2024 |
| Self-supervised Learning for Anomaly Detection in Computational Workflows H Jin, K Raghavan, G Papadimitriou, C Wang, A Mandal, E Deelman, ... arXiv preprint arXiv:2310.01247, 2023 | 2 | 2023 |
| A Tutorial of AMPL for Linear Programming H Jin | 2 | 2014 |
| ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain G Bernárdez, L Telyatnikov, M Montagna, F Baccini, M Papillon, ... Proceedings of the Geometry-grounded Representation Learning and Generative …, 2024 | 1 | 2024 |
| Massively Scalable, Resilient, and Adaptive Federated Learning System MS Mortazavi, H Jin, N Yan US Patent App. 18/159,571, 2023 | 1 | 2023 |
| Advancing anomaly detection in computational workflows with active learning K Raghavan, G Papadimitriou, H Jin, A Mandal, M Kiran, P Balaprakash, ... Future Generation Computer Systems 166, 107608, 2025 | | 2025 |
| FlowBench Raw Data Archive P George, H Jin, C Wang, K Raghavan, I Mahmud, K Thareja, P Zuk, ... Univ. of Southern California; Univ. of North Carolina at Chapel Hill …, 2024 | | 2024 |
| Robust Learning on Graphs H Jin University of Illinois at Chicago, 2022 | | 2022 |
| glmgen D Pinney, E Hale, M Havard, H Jin, A Fisher, B Palmintier, A Perrin, ... National Renewable Energy Laboratory (NREL), Golden, CO (United States), 2015 | | 2015 |
Xinhua ZhangUniversity of Illinois ChicagoVerified email at uic.edu
