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DIAL Publications

Preprints

  1. Yihong Ma, Yijun Tian, Nuno Moniz, Nitesh V. Chawla. “Class-Imbalanced Learning on Graphs: A Survey.” arXiv. PDF
  2. Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla. “Pure Message Passing Can Estimate Common Neighbor for Link Prediction.” arXiv. PDF
  3. Damien Dablain, Geoffrey Siwo, Nitesh V. Chawla. “Generative AI Design and Exploration of Nucleoside Analogs.” arXiv. PDF
  4. Andrés, José Á., Paul Czechowski, Erin K. Grey, Mandana Saebi, Kara J. Andres, Christopher Brown, Nitesh V. Chawla, et al. 2021. “Global Port Survey Quantifies Commercial Shipping’s Effect on Biodiversity.” bioRxiv (Cold Spring Harbor Laboratory), October. PDF
  5. Jian Xu, Mandana Saebi, Bruno Ribeiro, Lance M. Kaplan, Nitesh V. Chawla. “Detecting Anomalies in Sequential Data with Higher-order Networks.” arXiv
  6. Yuxiao Dong, Omar Lizardo, and Nitesh V. Chawla. “Do the Young Live in a “Smaller World” Than the Old? Age-Specific Degrees of Separation in a Large-Scale Mobile Communication Network.” arXiv
  7. Yang Yang, Jie Tang, Yuxiao Dong, Qiaozhu Mei, Reid A. Johnson, and Nitesh V. Chawla. “Modeling the Interplay Between Individual Behavior and Network Distributions.” arXiv

2024

  1. Joe Germino, Nuno Moniz, Nitesth V. Chawla. “FairMOE: counterfactually-fair mixture of experts with levels of interpretability”. Machine Learning. PDF
  2. Doheon Han, Nuno Moniz, Nitesh V Chawla, “AnyLoss: Transforming Classification Metrics into Loss Functions”. KDD ’24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. PDF
  3. Yihong Ma, Ning Yan, Jiayu Li, Masood Mortazavi, Nitesh V. Chawla. “Hetgpt: Harnessing the power of prompt tuning in pre-trained heterogeneous graph neural networks.” In Proceedings of the ACM on Web Conference 2024 (WWW’24). PDF
  4. Yihong Ma, Xiaobao Huang, Bozhao Nan, Nuno Moniz, Xiangliang Zhang, Olaf Wiest, and Nitesh V. Chawla. “Are we making much progress? Revisiting chemical reaction yield prediction from an imbalanced regression perspective.” In Companion Proceedings of the ACM on Web Conference 2024 (WWW’24). PDF
  5. Peiyu Li, Xiaobao Huang, Yijun Tian, Nitesth V. Chawla, “ChefFusion: Multimodal Foundation Model Integrating Recipe and Food Image Generation.” In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM’24). PDF
  6. Xiaobao Huang, Mihir Surve, Yuhan Liu, Tengfei Luo, Olaf Wiest, Xiangliang Zhang, and Nitesh V. Chawla. 2024. Application of Large Language Models in Chemistry Reaction Data Extraction and Cleaning. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (CIKM ’24). PDF
  7. Anna Sokol, Nuno Moniz, and Nitesh V. Chawla. 2024. Conformalized Selective Regression. In Proceedings of 8th International Conference on Data Science and Management of Data (CODS-COMAD ’24). PDF
  8. Yijun Tian, Huan Song, Zichen Wang, Haozhu Wang, Ziqing Hu, Fang Wang, Nitesh V. Chawla, Panpan Xu. Graph Neural Prompting with Large Language Models. Proceedings of the AAAI Conference on Artificial Intelligence, 38(17), 19080-19088. PDF

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
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