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ORCIDNitesh V. Chawla
Nitesh Vinay Chawla
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- affiliation: University of Notre Dame, USA
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2020 – today
- 2024
[j101]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)- [j100]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) - [j99]Damien Dablain, Colin Bellinger, Bartosz Krawczyk, David W. Aha, Nitesh V. Chawla:
Understanding imbalanced data: XAI & interpretable ML framework. Mach. Learn. 113(6): 3751-3769 (2024) - [j98]Damien Dablain, Kristen N. Jacobson, Colin Bellinger, Mark Roberts, Nitesh V. Chawla:
Understanding CNN fragility when learning with imbalanced data. Mach. Learn. 113(7): 4785-4810 (2024) - [j97]Joe Germino, Nuno Moniz, Nitesh V. Chawla:
FairMOE: counterfactually-fair mixture of experts with levels of interpretability. Mach. Learn. 113(9): 6539-6559 (2024) - [c239]Beenish Moalla Chaudhry, Muhammad Usama Islam, Nitesh Vinay Chawla:
Longitudinal Evaluation of Casual Puzzle Tablet Games by Older Adults. Conference on Designing Interactive Systems 2024 - [c238]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: 19080-19088 - [c237]Martin Michalowski, Robert Moskovitch, Nitesh V. Chawla:
Introduction to the Special Track on Artificial Intelligence and COVID-19 (Abstract Reprint). AAAI 2024: 22707 - [c236]Gonzalo A. Ruz, Nitesh V. Chawla:
SMOTE for gene regulatory network sampling. CIBCB 2024: 1-8 - [c235]Nitesh V. Chawla:
Traversing the Journey of Data and AI: From Convergence to Translation. CIKM 2024: 2 - [c234]Xiaobao Huang, Mihir Surve, Yuhan Liu, Tengfei Luo, Olaf Wiest, Xiangliang Zhang, Nitesh V. Chawla:
Application of Large Language Models in Chemistry Reaction Data Extraction and Cleaning. CIKM 2024: 3797-3801 - [c233]Peiyu Li, Xiaobao Huang, Yijun Tian, Nitesh V. Chawla:
ChefFusion: Multimodal Foundation Model Integrating Recipe and Food Image Generation. CIKM 2024: 3872-3876 - [c232]Jennifer J. Schnur, Angélica García-Martínez, Patrick Soga, Karla Badillo-Urquiola, Alejandra J. Botello, Ana Calderon Raisbeck, Sugana Chawla, Josef Ernst, William Gentry, Richard P. Johnson, Michael Kennel, Jesús Robles, Madison Wagner, Elizabeth Medina, Juan Garduño Espinosa, Horacio Márquez-González, Victor Olivar-López, Luis E. Juárez-Villegas, Martha Avilés-Robles, Elisa Dorantes-Acosta, Viridia Avila, Gina Chapa-Koloffon, Elizabeth Cruz, Leticia Luis, Clara Quezada, Emanuel Orozco, Edson Serván-Mori, Martha Cordero, Rubén Martín Payo, Nitesh V. Chawla:
SaludConectaMX: Lessons Learned from Deploying a Cooperative Mobile Health System for Pediatric Cancer Care in Mexico. CSCW Companion 2024: 316-322 - [c231]Damien A. Dablain, Nitesh V. Chawla:
Data Augmentation's Effect on Machine Learning Models when Learning with Imbalanced Data. DSAA 2024: 1-10 - [c230]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: 2365-2369 - [c229]Steven J. Krieg, Nitesh V. Chawla, Keith Feldman:
Representing Outcome-Driven Higher-Order Dependencies in Graphs of Disease Trajectories. ICHI 2024: 11-20 - [c228]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 - [c227]Guancheng Wan, Yijun Tian, Wenke Huang, Nitesh V. Chawla, Mang Ye:
S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning. ICML 2024 - [c226]Lirong Wu, Yijun Tian, Haitao Lin, Yufei Huang, Siyuan Li, Nitesh V. Chawla, Stan Z. Li:
Learning to Predict Mutational Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning. ICML 2024 - [c225]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. IJCAI 2024: 8048-8057 - [c224]Do Heon Han, Nuno Moniz, Nitesh V. Chawla:
AnyLoss: Transforming Classification Metrics into Loss Functions. KDD 2024: 992-1003 - [c223]Xiangchi Yuan, Yijun Tian, Chunhui Zhang, Yanfang Ye, Nitesh V. Chawla, Chuxu Zhang:
Graph Cross Supervised Learning via Generalized Knowledge. KDD 2024: 4083-4094 - [c222]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: 6312-6323 - [c221]Xubin Ren, Jiabin Tang, Dawei Yin, Nitesh V. Chawla, Chao Huang:
A Survey of Large Language Models for Graphs. KDD 2024: 6616-6626 - [c220]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: 6703 - [c219]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: 6749-6750 - [c218]Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla:
Pure Message Passing Can Estimate Common Neighbor for Link Prediction. NeurIPS 2024 - [c217]Kehan Guo, Bozhao Nan, Yujun Zhou, Taicheng Guo, Zhichun Guo, Mihir Surve, Zhenwen Liang, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang:
Can LLMs Solve Molecule Puzzles? A Multimodal Benchmark for Molecular Structure Elucidation. NeurIPS 2024 - [c216]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. NeurIPS 2024 - [c215]Zehong Wang, Zheyuan Zhang, Nitesh V. Chawla, Chuxu Zhang, Yanfang Ye:
GFT: Graph Foundation Model with Transferable Tree Vocabulary. NeurIPS 2024 - [c214]Zheyuan Liu, Xiaoxin He, Yijun Tian, Nitesh V. Chawla:
Can we Soft Prompt LLMs for Graph Learning Tasks? WWW (Companion Volume) 2024: 481-484 - [c213]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: 790-793 - [c212]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: 1015-1023 - [c211]Chao Huang, Xubin Ren, Jiabin Tang, Dawei Yin, Nitesh V. Chawla:
Large Language Models for Graphs: Progresses and Directions. WWW (Companion Volume) 2024: 1284-1287 - [i112]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 abs/2402.01680 (2024) - [i111]Yijun Tian, Yikun Han, Xiusi Chen, Wei Wang, Nitesh V. Chawla:
TinyLLM: Learning a Small Student from Multiple Large Language Models. CoRR abs/2402.04616 (2024) - [i110]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 abs/2402.05971 (2024) - [i109]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 abs/2402.07630 (2024) - [i108]Kaiwen Dong, Haitao Mao, Zhichun Guo, Nitesh V. Chawla:
Universal Link Predictor By In-Context Learning on Graphs. CoRR abs/2402.07738 (2024) - [i107]Yijun Tian, Chuxu Zhang, Ziyi Kou, Zheyuan Liu, Xiangliang Zhang, Nitesh V. Chawla:
UGMAE: A Unified Framework for Graph Masked Autoencoders. CoRR abs/2402.08023 (2024) - [i106]Zhichun Guo, Tong Zhao, Yozen Liu, Kaiwen Dong, William Shiao, Neil Shah, Nitesh V. Chawla:
Node Duplication Improves Cold-start Link Prediction. CoRR abs/2402.09711 (2024) - [i105]Zheyuan Liu, Xiaoxin He, Yijun Tian, Nitesh V. Chawla:
Can we Soft Prompt LLMs for Graph Learning Tasks? CoRR abs/2402.10359 (2024) - [i104]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 abs/2402.14391 (2024) - [i103]Anna Sokol, Nuno Moniz, Nitesh V. Chawla:
Conformalized Selective Regression. CoRR abs/2402.16300 (2024) - [i102]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 abs/2403.08820 (2024) - [i101]Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla:
You do not have to train Graph Neural Networks at all on text-attributed graphs. CoRR abs/2404.11019 (2024) - [i100]Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla:
CORE: Data Augmentation for Link Prediction via Information Bottleneck. CoRR abs/2404.11032 (2024) - [i99]Xubin Ren, Jiabin Tang, Dawei Yin, Nitesh V. Chawla, Chao Huang:
A Survey of Large Language Models for Graphs. CoRR abs/2405.08011 (2024) - [i98]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 abs/2405.10348 (2024) - [i97]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 abs/2405.11034 (2024) - [i96]Do Heon Han, Nuno Moniz, Nitesh V. Chawla:
AnyLoss: Transforming Classification Metrics into Loss Functions. CoRR abs/2405.14745 (2024) - [i95]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 abs/2405.17782 (2024) - [i94]Deng Pan, Nuno Moniz, Nitesh V. Chawla:
Fast Explainability via Feasible Concept Sets Generator. CoRR abs/2405.18664 (2024) - [i93]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 abs/2406.06777 (2024) - [i92]Damien A. Dablain, Nitesh V. Chawla:
The Hidden Influence of Latent Feature Magnitude When Learning with Imbalanced Data. CoRR abs/2407.10165 (2024) - [i91]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 abs/2407.10834 (2024) - [i90]Jennifer J. Schnur, Angélica García-Martínez, Patrick Soga, Karla Badillo-Urquiola, Alejandra J. Botello, Ana Calderon Raisbeck, Sugana Chawla, Josef Ernst, William Gentry, Richard P. Johnson, Michael Kennel, Jesús Robles, Madison Wagner, Elizabeth Medina, Juan Garduño Espinosa, Horacio Márquez-González, Victor Olivar-López, Luis E. Juárez-Villegas, Martha Avilés-Robles, Elisa Dorantes-Acosta, Viridia Avila, Gina Chapa-Koloffon, Elizabeth Cruz, Leticia Luis, Clara Quezada, Emanuel Orozco, Edson Serván-Mori, Martha Cordero, Rubén Martín Payo, Nitesh V. Chawla:
SaludConectaMX: Lessons Learned from Deploying a Cooperative Mobile Health System for Pediatric Cancer Care in Mexico. CoRR abs/2408.00881 (2024) - [i89]Peiyu Li, Xiaobao Huang, Yijun Tian, Nitesh V. Chawla:
ChefFusion: Multimodal Foundation Model Integrating Recipe and Food Image Generation. CoRR abs/2409.12010 (2024) - [i88]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:
Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge. CoRR abs/2410.02736 (2024) - [i87]Anna Sokol, Nuno Moniz, Elizabeth Daly, Michael Hind, Nitesh V. Chawla:
BenchmarkCards: Large Language Model and Risk Reporting. CoRR abs/2410.12974 (2024) - [i86]Khiem Le, Nitesh V. Chawla:
Utilizing Large Language Models in an iterative paradigm with Domain feedback for Zero-shot Molecule optimization. CoRR abs/2410.13147 (2024) - [i85]Yujun Zhou, Jingdong Yang, Kehan Guo, Pin-Yu Chen, Tian Gao, Werner Geyer, Nuno Moniz, Nitesh V. Chawla, Xiangliang Zhang:
LabSafety Bench: Benchmarking LLMs on Safety Issues in Scientific Labs. CoRR abs/2410.14182 (2024) - [i84]Grigorii Khvatskii, Nuno Moniz, Khoa Doan, Nitesh V. Chawla:
Class-Aware Contrastive Optimization for Imbalanced Text Classification. CoRR abs/2410.22197 (2024) - [i83]Zehong Wang, Zheyuan Zhang, Nitesh V. Chawla, Chuxu Zhang, Yanfang Ye:
GFT: Graph Foundation Model with Transferable Tree Vocabulary. CoRR abs/2411.06070 (2024) - [i82]Zheyuan Zhang, Zehong Wang, Tianyi Ma, Varun Sameer Taneja, Sofia Nelson, Nhi Ha Lan Le, Keerthiram Murugesan, Mingxuan Ju, Nitesh V. Chawla, Chuxu Zhang, Yanfang Ye:
MOPI-HFRS: A Multi-objective Personalized Health-aware Food Recommendation System with LLM-enhanced Interpretation. CoRR abs/2412.08847 (2024) - [i81]Zheyuan Zhang, Yiyang Li, Nhi Ha Lan Le, Zehong Wang, Tianyi Ma, Vincent Galassi, Keerthiram Murugesan, Nuno Moniz, Werner Geyer, Nitesh V. Chawla, Chuxu Zhang, Yanfang Ye:
NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning. CoRR abs/2412.15547 (2024) - [i80]Zehong Wang, Zheyuan Zhang, Tianyi Ma, Nitesh V. Chawla, Chuxu Zhang, Yanfang Ye:
Learning Cross-Task Generalities Across Graphs via Task-trees. CoRR abs/2412.16441 (2024) - 2023
- [j96]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): 46-54 (2023) - [j95]Jennifer J. Schnur, Nitesh V. Chawla:
Information fusion via symbolic regression: A tutorial in the context of human health. Inf. Fusion 92: 326-335 (2023) - [j94]Martin Michalowski, Robert Moskovitch, Nitesh V. Chawla:
Introduction to the Special Track on Artificial Intelligence and COVID-19. J. Artif. Intell. Res. 76: 523-525 (2023) - [j93]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): 1817-1830 (2023) - [j92]Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla:
DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data. IEEE Trans. Neural Networks Learn. Syst. 34(9): 6390-6404 (2023) - [j91]Daheng Wang, Tong Zhao, Wenhao Yu, Nitesh V. Chawla, Meng Jiang:
Deep Multimodal Complementarity Learning. IEEE Trans. Neural Networks Learn. Syst. 34(12): 10213-10224 (2023) - [j90]Zhichun Guo, Jun Tao, Siming Chen, Nitesh V. Chawla, Chaoli Wang:
SD2: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance. IEEE Trans. Vis. Comput. Graph. 29(8): 3569-3585 (2023) - [c210]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: 4893-4901 - [c209]Zhichun Guo, Chunhui Zhang, Yujie Fan, Yijun Tian, Chuxu Zhang, Nitesh V. Chawla:
Boosting Graph Neural Networks via Adaptive Knowledge Distillation. AAAI 2023: 7793-7801 - [c208]Yijun Tian, Kaiwen Dong, Chunhui Zhang, Chuxu Zhang, Nitesh V. Chawla:
Heterogeneous Graph Masked Autoencoders. AAAI 2023: 9997-10005 - [c207]Joe Germino, Nuno Moniz, Nitesh V. Chawla:
Fairness-Aware Mixture of Experts with Interpretability Budgets. DS 2023: 341-355 - [c206]Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk, Nitesh V. Chawla:
Efficient Augmentation for Imbalanced Deep Learning. ICDE 2023: 1433-1446 - [c205]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 - [c204]Steven J. Krieg, William C. Burgis, Patrick M. Soga, Nitesh V. Chawla:
Deep Ensembles for Graphs with Higher-order Dependencies. ICLR 2023 - [c203]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 - [c202]Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh V. Chawla, Neil Shah, Tong Zhao:
Linkless Link Prediction via Relational Distillation. ICML 2023: 12012-12033 - [c201]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: 6638-6646 - [c200]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: 5853-5854 - [c199]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: 5863-5864 - [c198]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 - [i79]Yijun Tian, Shichao Pei, Xiangliang Zhang, Chuxu Zhang, Nitesh V. Chawla:
Knowledge Distillation on Graphs: A Survey. CoRR abs/2302.00219 (2023) - [i78]Yihong Ma, Yijun Tian, Nuno Moniz, Nitesh V. Chawla:
Class-Imbalanced Learning on Graphs: A Survey. CoRR abs/2304.04300 (2023) - [i77]Damien A. Dablain, Nitesh V. Chawla:
Towards Understanding How Data Augmentation Works with Imbalanced Data. CoRR abs/2304.05895 (2023) - [i76]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 abs/2305.18365 (2023) - [i75]Jennifer J. Schnur, Nitesh V. Chawla:
Information Fusion via Symbolic Regression: A Tutorial in the Context of Human Health. CoRR abs/2306.00153 (2023) - [i74]Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla:
Pure Message Passing Can Estimate Common Neighbor for Link Prediction. CoRR abs/2309.00976 (2023) - [i73]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 abs/2309.15427 (2023) - [i72]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 abs/2310.04674 (2023) - [i71]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 abs/2310.15318 (2023) - [i70]Steven J. Krieg, Nitesh V. Chawla, Keith Feldman:
Representing Outcome-driven Higher-order Dependencies in Graphs of Disease Trajectories. CoRR abs/2312.15353 (2023) - 2022
- [j89]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): 25:1-25:27 (2022) - [j88]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): 36:1-36:27 (2022) - [j87]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: 12-21 (2022) - [j86]Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla:
AttrE2vec: Unsupervised attributed edge representation learning. Inf. Sci. 592: 82-96 (2022) - [j85]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: 107453 (2022) - [j84]Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla:
Graph Barlow Twins: A self-supervised representation learning framework for graphs. Knowl. Based Syst. 256: 109631 (2022) - [j83]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) - [j82]Beenish Moalla Chaudhry, Dipanwita Dasgupta, Nitesh V. Chawla:
Formative Evaluation of a Tablet Application to Support Goal-Oriented Care in Community-Dwelling Older Adults. Proc. ACM Hum. Comput. Interact. 6(MHCI): 1-21 (2022) - [j81]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): 97:1-97:21 (2022) - [c197]Yihong Ma, Patrick Gérard, Yijun Tian, Zhichun Guo, Nitesh V. Chawla:
Hierarchical Spatio-Temporal Graph Neural Networks for Pandemic Forecasting. CIKM 2022: 1481-1490 - [c196]Yiyue Qian, Yiming Zhang, Nitesh V. Chawla, Yanfang Ye, Chuxu Zhang:
Malicious Repositories Detection with Adversarial Heterogeneous Graph Contrastive Learning. CIKM 2022: 1645-1654 - [c195]