Hongwei Jin
Cited by
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Latent adversarial training of graph convolution networks
H Jin, X Zhang
ICML workshop on learning and reasoning with graph-structured representations 2, 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
Robust Training of Graph Convolutional Networks via Latent Perturbation
H Jin, X Zhang
European Conference on Machine Learning (ECML), 2020
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
Gromov-Wasserstein Discrepancy with Local Differential Privacy for Distributed Structural Graphs
H Jin, X Chen
Thirty-First International Joint Conference on Artificial Intelligence…, 2022
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
Certifying Robust Graph Classification under Orthogonal Gromov-Wasserstein Threats
H Jin, Z Yu, X Zhang
Advances in Neural Information Processing Systems, 2022
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
Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound
H Jin, Z Yu, X Zhang
The 38th Conference on Uncertainty in Artificial Intelligence, 2022
A Tutorial of AMPL for Linear Programming
H Jin
Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement
H Jin, P Balaprakash, A Zou, P Ghysels, AS Krishnapriyan, A Mate, ...
arXiv preprint arXiv:2405.10389, 2024
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
Advancing Anomaly Detection in Computational Workflows with Active Learning
K Raghavan, G Papadimitriou, H Jin, A Mandal, M Kiran, P Balaprakash, ...
arXiv preprint arXiv:2405.06133, 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
Massively Scalable, Resilient, and Adaptive Federated Learning System
MS Mortazavi, H Jin, N Yan
US Patent App. 18/159,571, 2023
Robust Learning on Graphs
H Jin
University of Illinois at Chicago, 2022
D Pinney, E Hale, M Havard, H Jin, A Fisher, B Palmintier, A Perrin, ...
National Renewable Energy Laboratory (NREL), Golden, CO (United States), 2015
Renewable Energy in Microgrid: A Stochastic Optimization Approach
H Jin
Illinois Institute of Technology, 2014
2022 IEEE/ACM Workshop on Workflows in Support of Large-Scale Science (WORKS)| 978-1-6654-5191-8/22/$31.00 2022 IEEE| DOI: 10.1109/WORKS56498. 2022.00017
I Abhinit, EK Adams, K Alam, A Alsaadi, A Amza, RM Badia, ...
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