Nitesh V. Chawla

ORCID

Publication Activity (10 Years)

Years Active: 1998-2024
Publications (10 Years): 296

Top Venues

Publications

Martin Michalowski, Robert Moskovitch, Nitesh V. Chawla
Introduction to the Special Track on Artificial Intelligence and COVID-19 (Abstract Reprint). AAAI (2024)
Xiangchi Yuan, Yijun Tian, Chunhui Zhang, Yanfang Ye, Nitesh V. Chawla, Chuxu Zhang
Graph Cross Supervised Learning via Generalized Knowledge. KDD (2024)
Yijun Tian, Chuxu Zhang, Ziyi Kou, Zheyuan Liu, Xiangliang Zhang, Nitesh V. Chawla
UGMAE: A Unified Framework for Graph Masked Autoencoders. CoRR (2024)
Yijun Tian, Huan Song, Zichen Wang, Haozhu Wang, Ziqing Hu, Fang Wang, Nitesh V. Chawla, Panpan Xu
Graph Neural Prompting with Large Language Models. AAAI (2024)
Kaiwen Dong, Haitao Mao, Zhichun Guo, Nitesh V. Chawla
Universal Link Predictor By In-Context Learning on Graphs. CoRR (2024)
Damien A. Dablain, Nitesh V. Chawla
The Hidden Influence of Latent Feature Magnitude When Learning with Imbalanced Data. CoRR (2024)
Damien Dablain, Colin Bellinger, Bartosz Krawczyk, David W. Aha, Nitesh V. Chawla
Understanding imbalanced data: XAI & interpretable ML framework. Mach. Learn. 113 (6) (2024)
Joe Germino, Annalisa Szymanski, Heather A. Eicher-Miller, Ronald A. Metoyer, Nitesh V. Chawla
Corrigendum: A community focused approach toward making healthy and affordable daily diet recommendations. Frontiers Big Data 7 (2024)
Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang
Large Language Model based Multi-Agents: A Survey of Progress and Challenges. CoRR (2024)
Anna Sokol, Nuno Moniz, Nitesh V. Chawla
Conformalized Selective Regression. CoRR (2024)
Joe Germino, Nuno Moniz, Nitesh V. Chawla
FairMOE: counterfactually-fair mixture of experts with levels of interpretability. Mach. Learn. 113 (9) (2024)
Zheyuan Zhang, Zehong Wang, Shifu Hou, Evan Hall, Landon Bachman, Jasmine White, Vincent Galassi, Nitesh V. Chawla, Chuxu Zhang, Yanfang Ye
Diet-ODIN: A Novel Framework for Opioid Misuse Detection with Interpretable Dietary Patterns. KDD (2024)
Do Heon Han, Nuno Moniz, Nitesh V. Chawla
AnyLoss: Transforming Classification Metrics into Loss Functions. KDD (2024)
Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla
You do not have to train Graph Neural Networks at all on text-attributed graphs. CoRR (2024)
Changsheng Ma, Taicheng Guo, Qiang Yang, Xiuying Chen, Xin Gao, Shangsong Liang, Nitesh V. Chawla, Xiangliang Zhang
A Property-Guided Diffusion Model For Generating Molecular Graphs. ICASSP (2024)
Do Heon Han, Nuno Moniz, Nitesh V. Chawla
AnyLoss: Transforming Classification Metrics into Loss Functions. CoRR (2024)
Chao Huang, Xubin Ren, Jiabin Tang, Dawei Yin, Nitesh V. Chawla
Large Language Models for Graphs: Progresses and Directions. WWW (Companion Volume) (2024)
Yuying Duan, Yijun Tian, Nitesh V. Chawla, Michael Lemmon
Post-Fair Federated Learning: Achieving Group and Community Fairness in Federated Learning via Post-processing. CoRR (2024)
Song Wang, Yushun Dong, Binchi Zhang, Zihan Chen, Xingbo Fu, Yinhan He, Cong Shen, Chuxu Zhang, Nitesh V. Chawla, Jundong Li
Safety in Graph Machine Learning: Threats and Safeguards. CoRR (2024)
Deng Pan, Nuno Moniz, Nitesh V. Chawla
Fast Explainability via Feasible Concept Sets Generator. CoRR (2024)
Yihong Ma, Xiaobao Huang, Bozhao Nan, Nuno Moniz, Xiangliang Zhang, Olaf Wiest, Nitesh V. Chawla
Are we making much progress? Revisiting chemical reaction yield prediction from an imbalanced regression perspective. CoRR (2024)
Xubin Ren, Jiabin Tang, Dawei Yin, Nitesh V. Chawla, Chao Huang
A Survey of Large Language Models for Graphs. KDD (2024)
Yihong Ma, Ning Yan, Jiayu Li, Masood S. Mortazavi, Nitesh V. Chawla
HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks. WWW (2024)
Yihong Ma, Xiaobao Huang, Bozhao Nan, Nuno Moniz, Xiangliang Zhang, Olaf Wiest, Nitesh V. Chawla
Are we Making Much Progress? Revisiting Chemical Reaction Yield Prediction from an Imbalanced Regression Perspective. WWW (Companion Volume) (2024)
Xubin Ren, Jiabin Tang, Dawei Yin, Nitesh V. Chawla, Chao Huang
A Survey of Large Language Models for Graphs. CoRR (2024)
Lirong Wu, Yijun Tian, Yufei Huang, Siyuan Li, Haitao Lin, Nitesh V. Chawla, Stan Z. Li
MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding. CoRR (2024)
Zheyuan Liu, Xiaoxin He, Yijun Tian, Nitesh V. Chawla
Can we Soft Prompt LLMs for Graph Learning Tasks? WWW (Companion Volume) (2024)
Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla
CORE: Data Augmentation for Link Prediction via Information Bottleneck. CoRR (2024)
Zheyuan Liu, Xiaoxin He, Yijun Tian, Nitesh V. Chawla
Can we soft prompt LLMs for graph learning tasks? CoRR (2024)
Lirong Wu, Yijun Tian, Yufei Huang, Siyuan Li, Haitao Lin, Nitesh V. Chawla, Stan Z. Li
MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding. ICLR (2024)
Damien Dablain, Kristen N. Jacobson, Colin Bellinger, Mark Roberts, Nitesh V. Chawla
Understanding CNN fragility when learning with imbalanced data. Mach. Learn. 113 (7) (2024)
Joe Germino, Annalisa Szymanski, Heather A. Eicher-Miller, Ronald A. Metoyer, Nitesh V. Chawla
A community focused approach toward making healthy and affordable daily diet recommendations. Frontiers Big Data 6 (2024)
Yijun Tian, Yikun Han, Xiusi Chen, Wei Wang, Nitesh V. Chawla
TinyLLM: Learning a Small Student from Multiple Large Language Models. CoRR (2024)
Quang H. Nguyen, Duy C. Hoang, Juliette Decugis, Saurav Manchanda, Nitesh V. Chawla, Khoa D. Doan
MetaLLM: A High-performant and Cost-efficient Dynamic Framework for Wrapping LLMs. CoRR (2024)
Khiem Le, Zhichun Guo, Kaiwen Dong, Xiaobao Huang, Bozhao Nan, Roshni G. Iyer, Xiangliang Zhang, Olaf Wiest, Wei Wang, Nitesh V. Chawla
MolX: Enhancing Large Language Models for Molecular Learning with A Multi-Modal Extension. CoRR (2024)
Lirong Wu, Yijun Tian, Haitao Lin, Yufei Huang, Siyuan Li, Nitesh V. Chawla, Stan Z. Li
Learning to Predict Mutation Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning. CoRR (2024)
Zheyuan Zhang, Zehong Wang, Shifu Hou, Evan Hall, Landon Bachman, Vincent Galassi, Jasmine White, Nitesh V. Chawla, Chuxu Zhang, Yanfang Ye
Diet-ODIN: A Novel Framework for Opioid Misuse Detection with Interpretable Dietary Patterns. CoRR (2024)
Leman Akoglu, Nitesh V. Chawla, Josep Domingo-Ferrer, Eren Kurshan, Senthil Kumar, Vidyut M. Naware, José A. Rodríguez-Serrano, Isha Chaturvedi, Saurabh Nagrecha, Mahashweta Das, Tanveer A. Faruquie
Machine Learning in Finance. KDD (2024)
Chuxu Zhang, Dongkuan Xu, Kaize Ding, Jundong Li, Mojan Javaheripi, Subhabrata Mukherjee, Nitesh V. Chawla, Huan Liu
RelKD 2024: The Second International Workshop on Resource-Efficient Learning for Knowledge Discovery. KDD (2024)
Zhichun Guo, Tong Zhao, Yozen Liu, Kaiwen Dong, William Shiao, Neil Shah, Nitesh V. Chawla
Node Duplication Improves Cold-start Link Prediction. CoRR (2024)
Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh V. Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, Bryan Hooi
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering. CoRR (2024)
Yihong Ma, Yijun Tian, Nuno Moniz, Nitesh V. Chawla
Class-Imbalanced Learning on Graphs: A Survey. CoRR (2023)
Jennifer J. Schnur, Nitesh V. Chawla
Information Fusion via Symbolic Regression: A Tutorial in the Context of Human Health. CoRR (2023)
Steven J. Krieg, Nitesh V. Chawla, Keith Feldman
Representing Outcome-driven Higher-order Dependencies in Graphs of Disease Trajectories. CoRR (2023)
Jennifer J. Schnur, Nitesh V. Chawla
Information fusion via symbolic regression: A tutorial in the context of human health. Inf. Fusion 92 (2023)
Steven J. Krieg, William C. Burgis, Patrick M. Soga, Nitesh V. Chawla
Deep Ensembles for Graphs with Higher-order Dependencies. ICLR (2023)
Damien A. Dablain, Nitesh V. Chawla
Towards Understanding How Data Augmentation Works with Imbalanced Data. CoRR (2023)
Taicheng Guo, Changsheng Ma, Xiuying Chen, Bozhao Nan, Kehan Guo, Shichao Pei, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang
Modeling non-uniform uncertainty in Reaction Prediction via Boosting and Dropout. CoRR (2023)
Zhichun Guo, Kehan Guo, Bozhao Nan, Yijun Tian, Roshni G. Iyer, Yihong Ma, Olaf Wiest, Xiangliang Zhang, Wei Wang, Chuxu Zhang, Nitesh V. Chawla
Graph-based Molecular Representation Learning. IJCAI (2023)
Chunhui Zhang, Yijun Tian, Mingxuan Ju, Zheyuan Liu, Yanfang Ye, Nitesh V. Chawla, Chuxu Zhang
Chasing All-Round Graph Representation Robustness: Model, Training, and Optimization. ICLR (2023)
Joe Germino, Nuno Moniz, Nitesh V. Chawla
Fairness-Aware Mixture of Experts with Interpretability Budgets. DS (2023)
Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla
DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data. IEEE Trans. Neural Networks Learn. Syst. 34 (9) (2023)
Yihong Ma, Md Nafee Al Islam, Jane Cleland-Huang, Nitesh V. Chawla
Detecting Anomalies in Small Unmanned Aerial Systems via Graphical Normalizing Flows. IEEE Intell. Syst. 38 (2) (2023)
Martin Michalowski, Robert Moskovitch, Nitesh V. Chawla
Introduction to the Special Track on Artificial Intelligence and COVID-19. J. Artif. Intell. Res. 76 (2023)
Daheng Wang, Zhihan Zhang, Yihong Ma, Tong Zhao, Tianwen Jiang, Nitesh V. Chawla, Meng Jiang
Modeling Co-Evolution of Attributed and Structural Information in Graph Sequence. IEEE Trans. Knowl. Data Eng. 35 (2) (2023)
Zhichun Guo, Jun Tao, Siming Chen, Nitesh V. Chawla, Chaoli Wang
: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance. IEEE Trans. Vis. Comput. Graph. 29 (8) (2023)
Zhichun Guo, Chunhui Zhang, Yujie Fan, Yijun Tian, Chuxu Zhang, Nitesh V. Chawla
Boosting Graph Neural Networks via Adaptive Knowledge Distillation. AAAI (2023)
Taicheng Guo, Kehan Guo, Bozhao Nan, Zhengwen Liang, Zhichun Guo, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang
What indeed can GPT models do in chemistry? A comprehensive benchmark on eight tasks. CoRR (2023)
Daheng Wang, Tong Zhao, Wenhao Yu, Nitesh V. Chawla, Meng Jiang
Deep Multimodal Complementarity Learning. IEEE Trans. Neural Networks Learn. Syst. 34 (12) (2023)
Yijun Tian, Kaiwen Dong, Chunhui Zhang, Chuxu Zhang, Nitesh V. Chawla
Heterogeneous Graph Masked Autoencoders. AAAI (2023)
Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk, Nitesh V. Chawla
Efficient Augmentation for Imbalanced Deep Learning. ICDE (2023)
Qiannan Zhang, Shichao Pei, Qiang Yang, Chuxu Zhang, Nitesh V. Chawla, Xiangliang Zhang
Cross-Domain Few-Shot Graph Classification with a Reinforced Task Coordinator. AAAI (2023)
Taicheng Guo, Kehan Guo, Bozhao Nan, Zhenwen Liang, Zhichun Guo, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang
What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks. NeurIPS (2023)
Yihong Ma, Ning Yan, Jiayu Li, Masood S. Mortazavi, Nitesh V. Chawla
HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks. CoRR (2023)
Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla
Pure Message Passing Can Estimate Common Neighbor for Link Prediction. CoRR (2023)
Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh V. Chawla, Neil Shah, Tong Zhao
Linkless Link Prediction via Relational Distillation. ICML (2023)
Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, Nitesh V. Chawla
Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency. ICLR (2023)
Yijun Tian, Huan Song, Zichen Wang, Haozhu Wang, Ziqing Hu, Fang Wang, Nitesh V. Chawla, Panpan Xu
Graph Neural Prompting with Large Language Models. CoRR (2023)
Derek Zhiyuan Cheng, Dhaval Patel, Linsey Pang, Sameep Mehta, Kexin Xie, Ed H. Chi, Wei Liu, Nitesh V. Chawla, James Bailey
Foundations and Applications in Large-scale AI Models: Pre-training, Fine-tuning, and Prompt-based Learning. KDD (2023)
Yijun Tian, Shichao Pei, Xiangliang Zhang, Chuxu Zhang, Nitesh V. Chawla
Knowledge Distillation on Graphs: A Survey. CoRR (2023)
Leman Akoglu, Nitesh V. Chawla, Senthil Kumar, Saurabh Nagrecha, Mahashweta Das, Vidyut M. Naware, Tanveer A. Faruquie
KDD Workshop on Machine Learning in Finance. KDD (2023)
Zhuoning Yuan, Zhishuai Guo, Nitesh V. Chawla, Tianbao Yang
Compositional Training for End-to-End Deep AUC Maximization. ICLR (2022)
Steven J. Krieg, Christian W. Smith, Rusha Chatterjee, Nitesh V. Chawla
Predicting Terrorist Attacks in the United States using Localized News Data. CoRR (2022)
Yihong Ma, Patrick Gérard, Yijun Tian, Zhichun Guo, Nitesh V. Chawla
Hierarchical Spatio-Temporal Graph Neural Networks for Pandemic Forecasting. CIKM (2022)
Yiyue Qian, Yiming Zhang, Nitesh V. Chawla, Yanfang Ye, Chuxu Zhang
Malicious Repositories Detection with Adversarial Heterogeneous Graph Contrastive Learning. CIKM (2022)
Yijun Tian, Kaiwen Dong, Chunhui Zhang, Chuxu Zhang, Nitesh V. Chawla
Heterogeneous Graph Masked Autoencoders. CoRR (2022)
Kaiwen Dong, Yijun Tian, Zhichun Guo, Yang Yang, Nitesh V. Chawla
FakeEdge: Alleviate Dataset Shift in Link Prediction. LoG (2022)
Kaize Ding, Jundong Li, Nitesh V. Chawla, Huan Liu
Graph Minimally-supervised Learning. WSDM (2022)
Steven J. Krieg, Carolina Avendano, Evan Grantham-Brown, Aaron Lilienfeld Asbun, Jennifer J. Schnur, Marie Lynn Miranda, Nitesh V. Chawla
Data-driven testing program improves detection of COVID-19 cases and reduces community transmission. npj Digit. Medicine 5 (2022)
Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, Nitesh V. Chawla
NOSMOG: Learning Noise-robust and Structure-aware MLPs on Graphs. CoRR (2022)
Kaize Ding, Chuxu Zhang, Jie Tang, Nitesh V. Chawla, Huan Liu
Toward Graph Minimally-Supervised Learning. KDD (2022)
Zhichun Guo, Bozhao Nan, Yijun Tian, Olaf Wiest, Chuxu Zhang, Nitesh V. Chawla
Graph-based Molecular Representation Learning. CoRR (2022)
Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla
Graph Barlow Twins: A self-supervised representation learning framework for graphs. Knowl. Based Syst. 256 (2022)
Damien Dablain, Colin Bellinger, Bartosz Krawczyk, Nitesh V. Chawla
Efficient Augmentation for Imbalanced Deep Learning. CoRR (2022)
Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla
Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning. CoRR (2022)
Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu
Few-Shot Learning on Graphs. IJCAI (2022)
Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh V. Chawla, Neil Shah, Tong Zhao
Linkless Link Prediction via Relational Distillation. CoRR (2022)
Piotr Bielak, Kamil Tagowski, Maciej Falkiewicz, Tomasz Kajdanowicz, Nitesh V. Chawla
FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings. Knowl. Based Syst. 236 (2022)
Senthil Kumar, Leman Akoglu, Nitesh V. Chawla, Saurabh Nagrecha, Vidyut M. Naware, Tanveer A. Faruquie, Hays McCormick
KDD Workshop on Machine Learning in Finance. KDD (2022)
Xian Wu, Chao Huang, Pablo Robles-Granda, Nitesh V. Chawla
Representation Learning on Variable Length and Incomplete Wearable-Sensory Time Series. ACM Trans. Intell. Syst. Technol. 13 (6) (2022)
Mandana Saebi, Steven Kreig, Chuxu Zhang, Meng Jiang, Tomasz Kajdanowicz, Nitesh V. Chawla
Heterogeneous relational reasoning in knowledge graphs with reinforcement learning. Inf. Fusion 88 (2022)
Jermaine Marshall, Priscilla Jiménez-Pazmino, Ronald A. Metoyer, Nitesh V. Chawla
A Survey on Healthy Food Decision Influences Through Technological Innovations. ACM Trans. Comput. Heal. 3 (2) (2022)
Zhichun Guo, Chunhui Zhang, Yujie Fan, Yijun Tian, Chuxu Zhang, Nitesh V. Chawla
Boosting Graph Neural Networks via Adaptive Knowledge Distillation. CoRR (2022)
Mary Jean Amon, Stephen M. Mattingly, Aaron Necaise, Gloria Mark, Nitesh V. Chawla, Anind K. Dey, Sidney D'Mello
Flexibility Versus Routineness in Multimodal Health Indicators: A Sensor-based Longitudinal in Situ Study of Information Workers. ACM Trans. Comput. Heal. 3 (3) (2022)
Yijun Tian, Chuxu Zhang, Zhichun Guo, Chao Huang, Ronald A. Metoyer, Nitesh V. Chawla
RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation. CoRR (2022)
Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla
AttrE2vec: Unsupervised attributed edge representation learning. Inf. Sci. 592 (2022)
Damien Dablain, Kristen N. Jacobson, Colin Bellinger, Mark Roberts, Nitesh V. Chawla
Understanding CNN Fragility When Learning With Imbalanced Data. CoRR (2022)
Kaiwen Dong, Yijun Tian, Zhichun Guo, Yang Yang, Nitesh V. Chawla
FakeEdge: Alleviate Dataset Shift in Link Prediction. CoRR (2022)
Yijun Tian, Chuxu Zhang, Zhichun Guo, Chao Huang, Ronald A. Metoyer, Nitesh V. Chawla
RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation. IJCAI (2022)
Steven J. Krieg, William C. Burgis, Patrick M. Soga, Nitesh V. Chawla
Deep Ensembles for Graphs with Higher-order Dependencies. CoRR (2022)

Nitesh V. Chawla.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Nitesh V. Chawla