Kaiwen Dong, Xiang Gao, Ayan Acharya, Maria Kissa, Hilaf Hasson, Mauricio Flores, Nitesh V. Chawla, Kamalika Das. “Transaction Categorization in QuickBooks with Relational Deep Learning.” arXiv
Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla. “You do not have to train Graph Neural Networks at all on text-attributed graphs.” arXiv
Yihong Ma, Yijun Tian, Nuno Moniz, Nitesh V. Chawla. “Class-Imbalanced Learning on Graphs: A Survey.” arXiv. PDF
Jonathan A. Karr Jr, Emory Smith, Matthew Hauenstein, Georgina Curto, Nitesh V. Chawla “What is Behind Homelessness Bias? Using LLMs and NLP to Mitigate Homelessness by Acting on Social Stigma.” PDF
Damien Dablain, Geoffrey Siwo, Nitesh V. Chawla. “Generative AI Design and Exploration of Nucleoside Analogs.” arXiv. PDF
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
2025
Anna Sokol, Elizabeth Daly, Michael Hind, David Piorkowski, Xiangliang Zhang, Nuno Moniz, Nitesh V Chawla.”BenchmarkCards: Standardized Documentation for Large Language Model Benchmarks.” arXiv
Anna Sokol, Matthew L. Sisk, Josefina Echavarría Alvarez, and Nitesh Chawla. 2025. Ventana a la Verdad (Window to the Truth): A Chatbot Application for Navigating The Colombian Truth Commission’s Archives. In Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining (WSDM ’25) PDF
Germino, Joe, Nuno Moniz, and Nitesh V. Chawla. “Intersectional Divergence: Measuring Fairness in Regression.” arXiv preprint arXiv:2505.00830 (2025). arXiv
Grigorii Khvatskii, Yong Suk Lee, Corey Angst, Maria Gibbs, Robert Landers, Nitesh V. Chawla “Do Multimodal Large Language Models Understand Welding?” PDF
Deng Pan, Nuno Moniz, Nitesh Chawla. 2025 “Fast Explanations via Policy Gradient-Optimized Explainer.” arXiv
Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla. “Pure Message Passing Can Estimate Common Neighbor for Link Prediction.” Poster
Tânia Carvalho, Nuno Moniz, Luís Antunes, Nitesh Chawla “Differentially-private data synthetisation for efficient re-identification risk control.” PDF
Dongwhi Kim, Nuno Moniz “Relevance-Aware Algorithmic Recourse.” PDF
Qinghua Zhao, Nuno Moniz, Alexis Korotasz, Carly Barbera, Jason R Rohr “Early warning signals of emerging infectious diseases.” PDF
Tânia Carvalho, Luís Antunes, Cristina Costa Santos, Nuno Moniz “Empowering open data sharing for social good: a privacy-aware approach.” PDF
Susana Valente, Mariana Ribeiro, Jennifer Schnur, Filipe Alves, Nuno Moniz, Dominik Seelow, João Parente Freixo, Paulo Filipe Silva, Jorge Oliveira “Analysis of Regions of Homozygosity: Revisited Through New Bioinformatic Approaches.” PDF
Xiaobao Huang, Yihong ma, Anjali Gurajapu, Jules Schleinitz, Zhichun Guo, Sarah E. Reisman, Nitesh V. Chawla “ChemHGNN: A Hierarchical Hypergraph Neural Network for Reaction Virtual Screening and Discovery.” arXiv
2024
Joe Germino, Nuno Moniz, Nitesth V. Chawla. “FairMOE: counterfactually-fair mixture of experts with levels of interpretability”. Machine Learning. PDF
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
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
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
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
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
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
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