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DIAL Publications

2023

  1. Ma, Yihong, Ning Yan, Jiayu Li, Masood Mortazavi, and Nitesh V. Chawla. “HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks.” ArXiv, (2023). PDF
  2. Dablain, Damien, Bartosz Krawczyk, and Nitesh V. Chawla. “DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data.” IEEE Transactions on Neural Networks and Learning Systems 34, no. 9 (September 1, 2023): 6390–6404. PDF
  3. Germino, Joe, Nuno Moniz, and Nitesh V. Chawla. “Fairness-Aware Mixture of Experts with Interpretability Budgets.” In Lecture Notes in Computer Science, 341–55, 2023. PDF
  4. Joe Germino, Annalisa Szymanski, Heather A Eicher-Miller, Ronald Metoyer, Nitesh V Chawla. “A community focused approach toward making healthy and affordable daily diet recommendations”. Frontiers in big Data. (2023). PDF
  5. Yihong Ma, Md Nafee Al Islam, Jane Cleland-Huang, Nitesh V. Chawla. “Detecting Anomalies in Small Unmanned Aerial Systems via Graphical Normalizing Flows.” IEEE Intelligent Systems 38(2): 46-54 (2023). PDF
  6. Steven J. Krieg, William C. Burgis, Patrick M. Soga, Nitesh V. Chawla. “Deep Ensembles for Graphs with Higher-order Dependencies.” Eleventh International Conference on Learning Representations (ICLR 2023) PDF
  7. Yijun Tian, Kaiwen Dong, Chunhui Zhang, Chuxu Zhang, Nitesh V. Chawla. “Heterogeneous Graph Masked Autoencoders.” The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI’ 2023) PDF
  8. Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, Nitesh V. Chawla. “Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency.” The Eleventh International Conference on Learning Representations (ICLR’ 2023) PDF
  9. Jennifer J. Schnur, Nitesh V. Chawla. “Information fusion via symbolic regression: A tutorial in the context of human health .” Information Fusion PDF
  10. Mandana Saebi, Bozhao Nan, John Herr, Jessica Wahlers, Zhichun Guo, Andrzej Zurański, Thierry Kogej, Per-Ola Norrby, Abigail Doyle, Olaf Wiest, Nitesh Chawla. “On the Use of Real-World Datasets for Reaction Yield Prediction.” Chemical Science 2023 PDF

2022

  1. Yihong Ma, Patrick Gerard, Yijun Tian, Zhichun Guo, Nitesh V. Chawla. “Hierarchical Spatio-Temporal Graph Neural Networks for Pandemic Forecasting.” 31st ACM International Conference on Information and Knowledge Management (CIKM’ 22) PDF
  2. Damien Dablain, Bartosz Krawczyk, Nitesh Chawla. “DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data.” IEEE Transactions on Neural Networks and Learning Systems 2022 PDF
  3. Kaiwen Dong,Yijun Tian,Zhichun Guo,Yang Yang,Nitesh V. Chawla. “FakeEdge: Alleviate Dataset Shift in Link Prediction.” Proceedings of the First Learning on Graphs Conference (LoG 2022) 2022 PDF
  4. Mandana Saebi, Steven Krieg, Chuxu Zhang, Meng Jiang, Nitesh Chawla. “Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning.” Information Fusion 88 (2022) PDF
  5. Steven J. Krieg, Christian W. Smith, Rusha Chatterjee, Nitesh V. Chawla. “Predicting terrorist attacks in the United States using localized news data.” PLoS one 17, no. 6 (2022) PDF
  6. Steven J. Krieg, Carolina Avendano, Evan Grantham-Brown, Aaron Lilienfeld Asbun, Jennifer J. Schnur, Marie L. Miranda, Nitesh V. Chawla. “Data-driven Testing Program Improves Detection of COVID-19 Cases and Reduces Community Transmission.” NPJ Digital Medicine 5(1), 17 PDF
  7. Steven J. Krieg*, Jennifer J. Schnur*, Marie L. Miranda, Micahel E. Pfrender, Nitesh V. Chawla. “Symptomatic, Presymptomatic, and Asymptomatic Transmission of SARSCoV-2 in a University Student Population, August–November 2020.” Public Health Reports 137, no. 5 (2022) PDF
  8. Zhaojun Wang, Mandana Saebi, Erin K Grey, James J Corbett. “Ballast water-mediated species spread risk dynamics and policy implications to reduce the invasion risk to the Mediterranean Sea.” Marine Pollution Bulletin 174 (2022) 2022 PDF
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