Chawla, Nitesh V

59 publications

IJCAI 2025 Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond Kehan Guo, Yili Shen, Gisela Abigail Gonzalez-Montiel, Yue Huang, Yujun Zhou, Mihir Surve, Zhichun Guo, Payel Das, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang

NeurIPS 2025 BenchmarkCards: Standardized Documentation for Large Language Model Benchmarks Anna Sokol, Elizabeth M. Daly, Michael Hind, David Piorkowski, Xiangliang Zhang, Nuno Moniz, Nitesh V Chawla

ICML 2025 Beyond Message Passing: Neural Graph Pattern Machine Zehong Wang, Zheyuan Zhang, Tianyi Ma, Nitesh V Chawla, Chuxu Zhang, Yanfang Ye

NeurIPS 2025 ChemOrch: Empowering LLMs with Chemical Intelligence via Groundbreaking Synthetic Instructions Yue Huang, Zhengzhe Jiang, Xiaonan Luo, Kehan Guo, Haomin Zhuang, Yujun Zhou, Zhengqing Yuan, Xiaoqi Sun, Jules Schleinitz, Yanbo Wang, Shuhao Zhang, Mihir Surve, Nitesh V Chawla, Olaf Wiest, Xiangliang Zhang

MLJ 2025 Differentially-Private Data Synthetisation for Efficient Re-Identification Risk Control Tânia Carvalho, Nuno Moniz, Luis Antunes, Nitesh V. Chawla

IJCAI 2025 Fast Explanations via Policy Gradient-Optimized Explainer Deng Pan, Nuno Moniz, Nitesh V. Chawla

ICLR 2025 Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge Jiayi Ye, Yanbo Wang, Yue Huang, Dongping Chen, Qihui Zhang, Nuno Moniz, Tian Gao, Werner Geyer, Chao Huang, Pin-Yu Chen, Nitesh V Chawla, Xiangliang Zhang

IJCAI 2025 Leveraging Artificial Intelligence to Bridge Gaps in Pediatric Oncology Care for Marginalized Spanish-Speaking Communities Grigorii Khvatskii, Angélica García-Martínez, Deng Pan, Matthew Belcher, Gerónimo Medrano Loera, Dayana Pineda Pérez, Juan Emmanuel Ferrari Muñoz-Ledo, Horacio Márquez-González, Nuno Moniz, Nitesh V. Chawla

TMLR 2025 Node Duplication Improves Cold-Start Link Prediction Zhichun Guo, Tong Zhao, Yozen Liu, Kaiwen Dong, William Shiao, Mingxuan Ju, Neil Shah, Nitesh V Chawla

ICML 2025 Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Zehong Wang, Zheyuan Zhang, Tianyi Ma, Nitesh V Chawla, Chuxu Zhang, Yanfang Ye

ECML-PKDD 2025 Transaction Categorization with Relational Deep Learning in QuickBooks Kaiwen Dong, Padmaja Jonnalagedda, Xiang Gao, Ayan Acharya, Maria Kissa, Mauricio Flores, Nitesh V. Chawla, Kamalika Das

TMLR 2025 Universal Link Predictor by In-Context Learning on Graphs Kaiwen Dong, Haitao Mao, Zhichun Guo, Nitesh V Chawla

IJCAI 2025 What Is Behind Homelessness Bias? Using LLMs and NLP to Mitigate Homelessness by Acting on Social Stigma Jonathan A. Karr Jr., Emory Smith, Matthew Hauenstein, Georgina Curto, Nitesh V. Chawla

ECML-PKDD 2025 WildInsight: A Chatbot for Wildlife Conservation Research Anna Sokol, Xiangliang Zhang, Nitesh V. Chawla

NeurIPS 2024 Can LLMs Solve Molecule Puzzles? a Multimodal Benchmark for Molecular Structure Elucidation Kehan Guo, Bozhao Nan, Yujun Zhou, Taicheng Guo, Zhichun Guo, Mihir Surve, Zhenwen Liang, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang

MLJ 2024 FairMOE: Counterfactually-Fair Mixture of Experts with Levels of Interpretability Joe Germino, Nuno Moniz, Nitesh V. Chawla

NeurIPS 2024 G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh V. Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, Bryan Hooi

NeurIPS 2024 GFT: Graph Foundation Model with Transferable Tree Vocabulary Zehong Wang, Zheyuan Zhang, Nitesh V Chawla, Chuxu Zhang, Yanfang Ye

AAAI 2024 Graph Neural Prompting with Large Language Models Yijun Tian, Huan Song, Zichen Wang, Haozhu Wang, Ziqing Hu, Fang Wang, Nitesh V. Chawla, Panpan Xu

AAAI 2024 Introduction to the Special Track on Artificial Intelligence and COVID-19 (Abstract Reprint) Martin Michalowski, Robert Moskovitch, Nitesh V. Chawla

NeurIPSW 2024 Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge Jiayi Ye, Yanbo Wang, Yue Huang, Dongping Chen, Qihui Zhang, Nuno Moniz, Tian Gao, Werner Geyer, Chao Huang, Pin-Yu Chen, Nitesh V Chawla, Xiangliang Zhang

IJCAI 2024 Large Language Model Based Multi-Agents: A Survey of Progress and Challenges Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang

ICML 2024 Learning to Predict Mutational Effects of Protein-Protein Interactions by Microenvironment-Aware Hierarchical Prompt Learning Lirong Wu, Yijun Tian, Haitao Lin, Yufei Huang, Siyuan Li, Nitesh V Chawla, Stan Z. Li

ICLR 2024 MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding Lirong Wu, Yijun Tian, Yufei Huang, Siyuan Li, Haitao Lin, Nitesh V Chawla, Stan Z. Li

NeurIPS 2024 Pure Message Passing Can Estimate Common Neighbor for Link Prediction Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla

ICML 2024 S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning Guancheng Wan, Yijun Tian, Wenke Huang, Nitesh V Chawla, Mang Ye

MLJ 2024 Understanding CNN Fragility When Learning with Imbalanced Data Damien Dablain, Kristen N. Jacobson, Colin Bellinger, Mark Roberts, Nitesh V. Chawla

MLJ 2024 Understanding Imbalanced Data: XAI & Interpretable ML Framework Damien Dablain, Colin Bellinger, Bartosz Krawczyk, David W. Aha, Nitesh V. Chawla

AAAI 2023 Boosting Graph Neural Networks via Adaptive Knowledge Distillation Zhichun Guo, Chunhui Zhang, Yujie Fan, Yijun Tian, Chuxu Zhang, Nitesh V. Chawla

AAAI 2023 Cross-Domain Few-Shot Graph Classification with a Reinforced Task Coordinator Qiannan Zhang, Shichao Pei, Qiang Yang, Chuxu Zhang, Nitesh V. Chawla, Xiangliang Zhang

IJCAI 2023 Graph-Based Molecular Representation Learning Zhichun Guo, Kehan Guo, Bozhao Nan, Yijun Tian, Roshni G. Iyer, Yihong Ma, Olaf Wiest, Xiangliang Zhang, Wei Wang, Chuxu Zhang, Nitesh V. Chawla

AAAI 2023 Heterogeneous Graph Masked Autoencoders Yijun Tian, Kaiwen Dong, Chunhui Zhang, Chuxu Zhang, Nitesh V. Chawla

JAIR 2023 Introduction to the Special Track on Artificial Intelligence and COVID-19 Martin Michalowski, Robert Moskovitch, Nitesh V. Chawla

ICML 2023 Linkless Link Prediction via Relational Distillation Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh V Chawla, Neil Shah, Tong Zhao

IJCAI 2022 Few-Shot Learning on Graphs Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu

IJCAI 2022 Recipe2Vec: Multi-Modal Recipe Representation Learning with Graph Neural Networks Yijun Tian, Chuxu Zhang, Zhichun Guo, Yihong Ma, Ronald A. Metoyer, Nitesh V. Chawla

IJCAI 2022 RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation Yijun Tian, Chuxu Zhang, Zhichun Guo, Chao Huang, Ronald A. Metoyer, Nitesh V. Chawla

ECML-PKDD 2021 An Optimized NL2SQL System for Enterprise Data Mart Kaiwen Dong, Kai Lu, Xin Xia, David A. Cieslak, Nitesh V. Chawla

AAAI 2020 Few-Shot Knowledge Graph Completion Chuxu Zhang, Huaxiu Yao, Chao Huang, Meng Jiang, Zhenhui Li, Nitesh V. Chawla

AAAI 2020 Graph Few-Shot Learning via Knowledge Transfer Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh V. Chawla, Zhenhui Li

AAAI 2020 Multi-Label Patent Categorization with Non-Local Attention-Based Graph Convolutional Network Pingjie Tang, Meng Jiang, Bryan (Ning) Xia, Jed W. Pitera, Jeffrey Welser, Nitesh V. Chawla

AAAI 2019 A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data Chuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang Feng, Cristian Lumezanu, Wei Cheng, Jingchao Ni, Bo Zong, Haifeng Chen, Nitesh V. Chawla

ECML-PKDD 2018 ONE-M: Modeling the Co-Evolution of Opinions and Network Connections Aastha Nigam, Kijung Shin, Ashwin Bahulkar, Bryan Hooi, David Hachen, Boleslaw K. Szymanski, Christos Faloutsos, Nitesh V. Chawla

JAIR 2018 SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-Year Anniversary Alberto Fernández, Salvador García, Francisco Herrera, Nitesh V. Chawla

IJCAI 2018 Task-Guided and Semantic-Aware Ranking for Academic Author-Paper Correlation Inference Chuxu Zhang, Lu Yu, Xiangliang Zhang, Nitesh V. Chawla

ECML-PKDD 2017 UAPD: Predicting Urban Anomalies from Spatial-Temporal Data Xian Wu, Yuxiao Dong, Chao Huang, Jian Xu, Dong Wang, Nitesh V. Chawla

ECML-PKDD 2015 Inferring Unusual Crowd Events from Mobile Phone Call Detail Records Yuxiao Dong, Fabio Pinelli, Yiannis Gkoufas, Zubair Nabi, Francesco Calabrese, Nitesh V. Chawla

ECML-PKDD 2015 The Evolution of Social Relationships and Strategies Across the Lifespan Yuxiao Dong, Nitesh V. Chawla, Jie Tang, Yang Yang, Yang Yang

ECML-PKDD 2015 Will This Paper Increase Your H-Index? Yuxiao Dong, Reid A. Johnson, Nitesh V. Chawla

ECML-PKDD 2013 How Long Will She Call Me? Distribution, Social Theory and Duration Prediction Yuxiao Dong, Jie Tang, Tiancheng Lou, Bin Wu, Nitesh V. Chawla

MLOSS 2011 LPmade: Link Prediction Made Easy Ryan N. Lichtenwalter, Nitesh V. Chawla

MLOSS 2009 Model Monitor (m2): Evaluating, Comparing, and Monitoring Models Troy Raeder, Nitesh V. Chawla

ECML-PKDD 2008 Learning Decision Trees for Unbalanced Data David A. Cieslak, Nitesh V. Chawla

AAAI 2007 Actively Exploring Creation of Face Space(s) for Improved Face Recognition Nitesh V. Chawla, Kevin W. Bowyer

JAIR 2005 Learning from Labeled and Unlabeled Data: An Empirical Study Across Techniques and Domains Nitesh V. Chawla, Grigoris I. Karakoulas

CVPR 2005 Random Subspaces and Subsampling for 2-D Face Recognition Nitesh V. Chawla, Kevin W. Bowyer

JMLR 2004 Learning Ensembles from Bites: A Scalable and Accurate Approach Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer

JAIR 2002 SMOTE: Synthetic Minority Over-Sampling Technique Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, W. Philip Kegelmeyer

CVPR 2001 Bagging Is a Small-Data-Set Phenomenon Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, W. Philip Kegelmeyer