| Asynchronous online federated learning for edge devices with non-iid data Y Chen, Y Ning, M Slawski, H Rangwala 2020 IEEE International Conference on Big Data (Big Data), 15-24, 2020 | 521 | 2020 |
| Learning dynamic context graphs for predicting social events S Deng, H Rangwala, Y Ning Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 123 | 2019 |
| Dynamic knowledge graph based multi-event forecasting S Deng, H Rangwala, Y Ning Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 119 | 2020 |
| Cola-GNN: Cross-location attention based graph neural networks for long-term ILI prediction S Deng, S Wang, H Rangwala, L Wang, Y Ning Proceedings of the 29th ACM international conference on information …, 2020 | 117 | 2020 |
| Algorithmic fairness in computational medicine J Xu, Y Xiao, WH Wang, Y Ning, EA Shenkman, J Bian, F Wang eBioMedicine, Part of THE LANCET Discovery Science 84, 2022 | 104 | 2022 |
| Modeling Precursors for Event Forecasting via Nested Multi-Instance Learning Y Ning, S Muthiah, H Rangwala, N Ramakrishnan Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and …, 2016 | 75 | 2016 |
| Collaborative graph learning with auxiliary text for temporal event prediction in healthcare C Lu, CK Reddy, P Chakraborty, S Kleinberg, Y Ning Proceedings of the Thirtieth International Joint Conference on Artificial …, 2021 | 66 | 2021 |
| Empirical analysis of multi-task learning for reducing identity bias in toxic comment detection A Vaidya, F Mai, Y Ning Proceedings of the International AAAI Conference on Web and Social Media 14 …, 2020 | 65 | 2020 |
| A multiple instance learning framework for identifying key sentences and detecting events W Wang, Y Ning, H Rangwala, N Ramakrishnan Proceedings of the 25th ACM International on Conference on Information and …, 2016 | 52 | 2016 |
| Certified edge unlearning for graph neural networks K Wu, J Shen, Y Ning, T Wang, WH Wang Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 41 | 2023 |
| Context-aware health event prediction via transition functions on dynamic disease graphs C Lu, T Han, Y Ning Proceedings of the AAAI Conference on Artificial Intelligence 36 (4), 4567-4574, 2022 | 37 | 2022 |
| STAPLE: Spatio-Temporal Precursor Learning for Event Forecasting Y Ning, R Tao, CK Reddy, H Rangwala, JC Starz, N Ramakrishnan The 18th SIAM International Conference on Data Mining (SDM18), 2018 | 32 | 2018 |
| Federated multi-task learning with hierarchical attention for sensor data analytics Y Chen, Y Ning, Z Chai, H Rangwala 2020 International Joint Conference on Neural Networks (IJCNN), 1-8, 2020 | 31 | 2020 |
| Graph message passing with cross-location attentions for long-term ili prediction S Deng, S Wang, H Rangwala, L Wang, Y Ning arXiv preprint arXiv:1912.10202, 2019 | 30 | 2019 |
| Self-supervised graph learning with hyperbolic embedding for temporal health event prediction C Lu, CK Reddy, Y Ning IEEE Transactions on Cybernetics 53 (4), 2124-2136, 2021 | 29 | 2021 |
| Generating Realistic Synthetic Population Datasets H Wu, Y Ning, P Chakraborty, J Vreeken, N Tatti, N Ramakrishnan ACM Transactions on Knowledge Discovery from Data (TKDD), 2018 | 28 | 2018 |
| A Gradient-based Adaptive Learning Framework for Efficient Personal Recommendation Y Ning, Y Shi, L Hong, H Rangwala, N Ramakrishnan Proceedings of the 11th ACM Conference on Recommender Systems (RecSys’17), 2017 | 27 | 2017 |
| Multi-label clinical time-series generation via conditional GAN C Lu, CK Reddy, P Wang, D Nie, Y Ning IEEE Transactions on Knowledge and Data Engineering, 2023 | 26 | 2023 |
| Incorporating relational knowledge in explainable fake news detection K Wu, X Yuan, Y Ning Pacific-Asia Conference on Knowledge Discovery and Data Mining, 403-415, 2021 | 25 | 2021 |
| Fairness of classification using users’ social relationships in online peer-to-peer lending Y Li, Y Ning, R Liu, Y Wu, W Hui Wang Companion Proceedings of the Web Conference 2020, 733-742, 2020 | 24 | 2020 |
Huzefa RangwalaProfessor of Computer Science, George Mason/ ML Scientist, AmazonΗ διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα cs.gmu.edu
