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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
- 2025
[j102]Grigorii Khvatskii, Yong Suk Lee, Corey Angst, Maria Gibbs, Robert Landers, Nitesh V. Chawla:
Do multimodal large language models understand welding? Inf. Fusion 120: 103121 (2025)- [c245]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. ICLR 2025 - [c244]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. KDD (1) 2025: 2860-2871 - [c243]Yijun Tian, Yikun Han, Xiusi Chen, Wei Wang, Nitesh V. Chawla:
Beyond Answers: Transferring Reasoning Capabilities to Smaller LLMs Using Multi-Teacher Knowledge Distillation. WSDM 2025: 251-260 - [c242]Anna Sokol, Matthew L. Sisk, Josefina Echavarría Alvarez, Nitesh V. Chawla:
Ventana a la Verdad (Window to the Truth): A Chatbot Application for Navigating The Colombian Truth Commission's Archives. WSDM 2025: 1052-1055 - [c241]Xiangqi Wang, Tianyu Yang, Jason Rohr, Brett Scheffers, Nitesh V. Chawla, Xiangliang Zhang:
WildlifeLookup: A Chatbot Facilitating Wildlife Management with Accessible Data and Insights. WSDM 2025: 1064-1067 - [i117]Zehong Wang, Zheyuan Zhang, Tianyi Ma, Nitesh V. Chawla, Chuxu Zhang, Yanfang Ye:
Neural Graph Pattern Machine. CoRR abs/2501.18739 (2025) - [i116]Kehan Guo, Yili Shen, Gisela Abigail Gonzalez-Montiel, Yue Huang, Yujun Zhou, Mihir Surve, Zhichun Guo, Prayel Das, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang:
Artificial Intelligence in Spectroscopy: Advancing Chemistry from Prediction to Generation and Beyond. CoRR abs/2502.09897 (2025) - [i115]Yue Huang, Chujie Gao, Siyuan Wu, Haoran Wang, Xiangqi Wang, Yujun Zhou, Yanbo Wang, Jiayi Ye, Jiawen Shi, Qihui Zhang, Yuan Li, Han Bao, Zhaoyi Liu, Tianrui Guan, Dongping Chen, Ruoxi Chen, Kehan Guo, Andy Zou, Bryan Hooi Kuen-Yew, Caiming Xiong, Elias Stengel-Eskin, Hongyang Zhang, Hongzhi Yin, Huan Zhang, Huaxiu Yao, Jaehong Yoon, Jieyu Zhang, Kai Shu, Kaijie Zhu, Ranjay Krishna, Swabha Swayamdipta, Taiwei Shi, Weijia Shi, Xiang Li, Yiwei Li, Yuexing Hao, Zhihao Jia, Zhize Li, Xiuying Chen, Zhengzhong Tu, Xiyang Hu, Tianyi Zhou, Jieyu Zhao, Lichao Sun, Furong Huang, Or Cohen Sasson, Prasanna Sattigeri, Anka Reuel, Max Lamparth, Yue Zhao, Nouha Dziri, Yu Su, Huan Sun, Heng Ji, Chaowei Xiao, Mohit Bansal, Nitesh V. Chawla, Jian Pei, Jianfeng Gao, Michael Backes, Philip S. Yu, Neil Zhenqiang Gong, Pin-Yu Chen, Bo Li, Xiangliang Zhang:
On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective. CoRR abs/2502.14296 (2025) - [i114]Si Chen, Reid Metoyer, Khiem Le, Adam Acunin, Izzy Molnar, Alex Ambrose, James Lang, Nitesh V. Chawla, Ronald A. Metoyer:
Bridging the AI Adoption Gap: Designing an Interactive Pedagogical Agent for Higher Education Instructors. CoRR abs/2503.05039 (2025) - [i113]Grigorii Khvatskii, Yong Suk Lee, Corey Angst, Maria Gibbs, Robert Landers, Nitesh V. Chawla:
Do Multimodal Large Language Models Understand Welding? CoRR abs/2503.16537 (2025) - 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) - [c240]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 - [c239]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 - [c238]Martin Michalowski, Robert Moskovitch, Nitesh V. Chawla:
Introduction to the Special Track on Artificial Intelligence and COVID-19 (Abstract Reprint). AAAI 2024: 22707 - [c237]Gonzalo A. Ruz, Nitesh V. Chawla:
SMOTE for gene regulatory network sampling. CIBCB 2024: 1-8 - [c236]Nitesh V. Chawla:
Traversing the Journey of Data and AI: From Convergence to Translation. CIKM 2024: 2 - [c235]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 - [c234]Peiyu Li, Xiaobao Huang, Yijun Tian, Nitesh V. Chawla:
ChefFusion: Multimodal Foundation Model Integrating Recipe and Food Image Generation. CIKM 2024: 3872-3876 - [c233]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 - [c232]Damien A. Dablain, Nitesh V. Chawla:
Data Augmentation's Effect on Machine Learning Models when Learning with Imbalanced Data. DSAA 2024: 1-10 - [c231]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 - [c230]Steven J. Krieg, Nitesh V. Chawla, Keith Feldman:
Representing Outcome-Driven Higher-Order Dependencies in Graphs of Disease Trajectories. ICHI 2024: 11-20 - [c229]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 - [c228]Guancheng Wan, Yijun Tian, Wenke Huang, Nitesh V. Chawla, Mang Ye:
S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning. ICML 2024 - [c227]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 - [c226]Damien A. Dablain, Nitesh V. Chawla:
The Hidden Influence of Latent Feature Magnitude When Learning with Imbalanced Data. ICPRAI (1) 2024: 76-91 - [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]Zhuoning Yuan, Zhishuai Guo, Nitesh V. Chawla, Tianbao Yang:
Compositional Training for End-to-End Deep AUC Maximization. ICLR 2022 - [c194]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: 3466-3472 - [c193]Yijun Tian, Chuxu Zhang, Zhichun Guo, Yihong Ma, Ronald A. Metoyer, Nitesh V. Chawla:
Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks. IJCAI 2022: 3473-3479 - [c192]Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu:
Few-Shot Learning on Graphs. IJCAI 2022: 5662-5669 - [c191]Kaize Ding, Chuxu Zhang, Jie Tang, Nitesh V. Chawla, Huan Liu:
Toward Graph Minimally-Supervised Learning. KDD 2022: 4782-4783 - [c190]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: 4882-4883 - [c189]Kaiwen Dong, Yijun Tian, Zhichun Guo, Yang Yang, Nitesh V. Chawla:
FakeEdge: Alleviate Dataset Shift in Link Prediction. LoG 2022: 56 - [c188]Md Nafee Al Islam, Yihong Ma, Pedro Alarcon Granadeno, Nitesh V. Chawla, Jane Cleland-Huang:
RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers. RE 2022: 153-165 - [c187]Kaize Ding, Jundong Li, Nitesh V. Chawla, Huan Liu:
Graph Minimally-supervised Learning. WSDM 2022: 1620-1622 - [e10]Peipei Li, Kui Yu, Nitesh V. Chawla, Ronen Feldman, Qing Li, Xindong Wu:
IEEE International Conference on Knowledge Graph, ICKG 2022, Orlando, FL, USA, November 30 - Dec. 1, 2022. IEEE 2022, ISBN 978-1-6654-5101-7 [contents] - [i69]Steven J. Krieg, Christian W. Smith, Rusha Chatterjee, Nitesh V. Chawla:
Predicting Terrorist Attacks in the United States using Localized News Data. CoRR abs/2201.04292 (2022) - [i68]Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu:
Few-Shot Learning on Graphs: A Survey. CoRR abs/2203.09308 (2022) - [i67]Zhichun Guo, Jun Tao, Siming Chen, Nitesh V. Chawla, Chaoli Wang:
SD2: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance. CoRR abs/2203.12671 (2022) - [i66]Yijun Tian, Chuxu Zhang, Zhichun Guo, Yihong Ma, Ronald A. Metoyer, Nitesh V. Chawla:
Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks. CoRR abs/2205.12396 (2022) - [i65]Steven J. Krieg, William C. Burgis, Patrick M. Soga, Nitesh V. Chawla:
Deep Ensembles for Graphs with Higher-order Dependencies. CoRR abs/2205.13988 (2022) - [i64]Yijun Tian, Chuxu Zhang, Zhichun Guo, Chao Huang, Ronald A. Metoyer, Nitesh V. Chawla:
RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation. CoRR abs/2205.14005 (2022) - [i63]Zhichun Guo, Bozhao Nan, Yijun Tian, Olaf Wiest, Chuxu Zhang, Nitesh V. Chawla:
Graph-based Molecular Representation Learning. CoRR abs/2207.04869 (2022) - [i62]Damien Dablain, Colin Bellinger, Bartosz Krawczyk, Nitesh V. Chawla:
Efficient Augmentation for Imbalanced Deep Learning. CoRR abs/2207.06080 (2022) - [i61]Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla:
Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning. CoRR abs/2207.06084 (2022) - [i60]Md Nafee Al Islam, Yihong Ma, Pedro Alarcon Granadeno, Nitesh V. Chawla, Jane Cleland-Huang:
RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers. CoRR abs/2207.08857 (2022) - [i59]Yijun Tian, Kaiwen Dong, Chunhui Zhang, Chuxu Zhang, Nitesh V. Chawla:
Heterogeneous Graph Masked Autoencoders. CoRR abs/2208.09957 (2022) - [i58]Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, Nitesh V. Chawla:
NOSMOG: Learning Noise-robust and Structure-aware MLPs on Graphs. CoRR abs/2208.10010 (2022) - [i57]Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh V. Chawla, Neil Shah, Tong Zhao:
Linkless Link Prediction via Relational Distillation. CoRR abs/2210.05801 (2022) - [i56]Zhichun Guo, Chunhui Zhang, Yujie Fan, Yijun Tian, Chuxu Zhang, Nitesh V. Chawla:
Boosting Graph Neural Networks via Adaptive Knowledge Distillation. CoRR abs/2210.05920 (2022) - [i55]Damien Dablain, Kristen N. Jacobson, Colin Bellinger, Mark Roberts, Nitesh V. Chawla:
Understanding CNN Fragility When Learning With Imbalanced Data. CoRR abs/2210.09465 (2022) - [i54]Kaiwen Dong, Yijun Tian, Zhichun Guo, Yang Yang, Nitesh V. Chawla:
FakeEdge: Alleviate Dataset Shift in Link Prediction. CoRR abs/2211.15899 (2022) - [i53]Tânia Carvalho, Nuno Moniz, Pedro Faria, Luís Antunes, Nitesh V. Chawla:
Privacy-Preserving Data Synthetisation for Secure Information Sharing. CoRR abs/2212.00484 (2022) - [i52]Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk, David W. Aha, Nitesh V. Chawla:
Interpretable ML for Imbalanced Data. CoRR abs/2212.07743 (2022) - 2021
- [j80]Pablo Robles-Granda, Suwen Lin, Xian Wu, Gonzalo J. Martínez, Stephen M. Mattingly, Edward Moskal, Aaron Striegel, Nitesh V. Chawla, Sidney D'Mello, Julie M. Gregg, Kari Nies, Gloria Mark, Ted Grover, Andrew T. Campbell, Shayan Mirjafari, Koustuv Saha, Munmun De Choudhury, Anind K. Dey:
Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being. IEEE Comput. Intell. Mag. 16(2): 46-61 (2021) - [j79]Steven J. Krieg, Jennifer J. Schnur, Jermaine D. Marshall, Matthew M. Schoenbauer, Nitesh V. Chawla:
Pandemic Pulse: Unraveling and Modeling Social Signals During the COVID-19 Pandemic. Digit. Gov. Res. Pract. 2(2): 19:1-19:9 (2021) - [j78]Munira Syed, Daheng Wang, Meng Jiang, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla:
Unified Representation of Twitter and Online News Using Graph and Entities. Frontiers Big Data 4: 699070 (2021) - [j77]Yijun Tian, Chuxu Zhang, Ronald A. Metoyer, Nitesh V. Chawla:
Recipe Recommendation With Hierarchical Graph Attention Network. Frontiers Big Data 4: 778417 (2021) - [j76]Louis Faust, Keith Feldman, Suwen Lin, Stephen M. Mattingly, Sidney D'Mello, Nitesh V. Chawla:
Examining Response to Negative Life Events Through Fitness Tracker Data. Frontiers Digit. Health 3: 659088 (2021) - [j75]Shayan Mirjafari, Hessam Bagherinezhad, Subigya Nepal, Gonzalo J. Martínez, Koustuv Saha, Mikio Obuchi, Pino G. Audia, Nitesh V. Chawla, Anind K. Dey, Aaron Striegel, Andrew T. Campbell:
Predicting Job Performance Using Mobile Sensing. IEEE Pervasive Comput. 20(4): 43-51 (2021) - [j74]Tianwen Jiang, Qingkai Zeng, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang:
Biomedical Knowledge Graphs Construction From Conditional Statements. IEEE ACM Trans. Comput. Biol. Bioinform. 18(3): 823-835 (2021) - [j73]Daheng Wang, Qingkai Zeng, Nitesh V. Chawla, Meng Jiang:
Modeling Complementarity in Behavior Data with Multi-Type Itemset Embedding. ACM Trans. Intell. Syst. Technol. 12(4): 42:1-42:25 (2021) - [j72]Chuxu Zhang, Huaxiu Yao, Lu Yu, Chao Huang, Dongjin Song, Haifeng Chen, Meng Jiang, Nitesh V. Chawla:
Inductive Contextual Relation Learning for Personalization. ACM Trans. Inf. Syst. 39(3): 35:1-35:22 (2021) - [c186]Yijun Tian, Chuxu Zhang, Ronald A. Metoyer, Nitesh V. Chawla:
Recipe Representation Learning with Networks. CIKM 2021: 1824-1833 - [c185]Suwen Lin, Xian Wu, Nitesh V. Chawla:
motif2vec: Semantic-aware Representation Learning for Wearables' Time Series Data. DSAA 2021: 1-10 - [c184]Beenish M. Chaudhry, Dipanwita Dasgupta, Mona A. Mohamed, Nitesh V. Chawla:
Teaching Tablet Technology to Older Adults. HCI (42) 2021: 168-182 - [c183]Dipanwita Dasgupta, Beenish M. Chaudhry, Nitesh V. Chawla:
A Qualitative Usability Evaluation of Tablets and Accessibility Settings by Older Adults. HCI (42) 2021: 183-204 - [c182]Daheng Wang, Tong Zhao, Nitesh V. Chawla, Meng Jiang:
Dynamic Attributed Graph Prediction with Conditional Normalizing Flows. ICDM 2021: 1385-1390 - [c181]Senthil Kumar, Leman Akoglu, Nitesh V. Chawla, José A. Rodríguez-Serrano, Tanveer A. Faruquie, Saurabh Nagrecha:
Machine Learning in Finance. KDD 2021: 4139-4140 - [c180]Suwen Lin, Louis Faust, Nitesh V. Chawla:
Lan: Learning to Augment Noise Tolerance for Self-report Survey Labels. PerCom 2021: 1-10 - [c179]Kaiwen Dong, Kai Lu, Xin Xia, David A. Cieslak, Nitesh V. Chawla:
An Optimized NL2SQL System for Enterprise Data Mart. ECML/PKDD (5) 2021: 335-350 - [c178]Zhichun Guo, Chuxu Zhang, Wenhao Yu, John Herr, Olaf Wiest, Meng Jiang, Nitesh V. Chawla:
Few-Shot Graph Learning for Molecular Property Prediction. WWW 2021: 2559-2567 - [i51]Zhichun Guo, Chuxu Zhang, Wenhao Yu, John Herr, Olaf Wiest, Meng Jiang, Nitesh V. Chawla:
Few-Shot Graph Learning for Molecular Property Prediction. CoRR abs/2102.07916 (2021) - [i50]Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla:
DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data. CoRR abs/2105.02340 (2021) - [i49]Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla:
Graph Barlow Twins: A self-supervised representation learning framework for graphs. CoRR abs/2106.02466 (2021) - 2020
- [j71]Suman Kundu, Tomasz Kajdanowicz, Przemyslaw Kazienko, Nitesh V. Chawla:
Fuzzy Relative Willingness: Modeling Influence of Exogenous Factors in Driving Information Propagation Through a Social Network. IEEE Access 8: 186653-186662 (2020) - [j70]Mandana Saebi, Giovanni Luca Ciampaglia, Lance M. Kaplan, Nitesh V. Chawla:
HONEM: Learning Embedding for Higher Order Networks. Big Data 8(4): 255-269 (2020) - [j69]Mandana Saebi, Jian Xu, Lance M. Kaplan, Bruno Ribeiro, Nitesh V. Chawla:
Efficient modeling of higher-order dependencies in networks: from algorithm to application for anomaly detection. EPJ Data Sci. 9(1): 15 (2020) - [j68]Louis Faust, Keith Feldman, Stephen M. Mattingly, David Hachen, Nitesh V. Chawla:
Deviations from normal bedtimes are associated with short-term increases in resting heart rate. npj Digit. Medicine 3 (2020) - [j67]Chao Huang, Dong Wang, Nitesh V. Chawla:
Scalable Uncertainty-Aware Truth Discovery in Big Data Social Sensing Applications for Cyber-Physical Systems. IEEE Trans. Big Data 6(4): 702-713 (2020) - [c177]Chuxu Zhang, Huaxiu Yao, Chao Huang, Meng Jiang, Zhenhui Li, Nitesh V. Chawla:
Few-Shot Knowledge Graph Completion. AAAI 2020: 3041-3048 - [c176]Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh V. Chawla, Zhenhui Li:
Graph Few-Shot Learning via Knowledge Transfer. AAAI 2020: 6656-6663 - [c175]Pingjie Tang, Meng Jiang, Bryan (Ning) Xia, Jed W. Pitera, Jeffrey Welser, Nitesh V. Chawla:
Multi-Label Patent Categorization with Non-Local Attention-Based Graph Convolutional Network. AAAI 2020: 9024-9031 - [c174]Munira Syed, Daheng Wang, Meng Jiang, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla:
Overcoming Data Sparsity in Predicting User Characteristics from Behavior through Graph Embeddings. ASONAM 2020: 32-36 - [c173]Jennifer J. Schnur, Ryan Karl, Angélica García-Martínez, Meng Jiang, Nitesh V. Chawla:
Imputing Growth Snapshot Similarity in Early Childhood Development: A Tensor Decomposition Approach. BIBM 2020: 729-734 - [c172]Suwen Lin, Louis Faust, Sidney D'Mello, Gonzalo J. Martínez, Nitesh V. Chawla:
MBead: Semi-supervised Multilabel Behaviour Anomaly Detection on Multivariate Temporal Sensory Data. IEEE BigData 2020: 1089-1096 - [c171]Zhichun Guo, Wenhao Yu, Chuxu Zhang, Meng Jiang, Nitesh V. Chawla:
GraSeq: Graph and Sequence Fusion Learning for Molecular Property Prediction. CIKM 2020: 435-443 - [c170]Xian Wu, Stephen M. Mattingly, Shayan Mirjafari, Chao Huang, Nitesh V. Chawla:
Personalized Imputation on Wearable-Sensory Time Series via Knowledge Transfer. CIKM 2020: 1625-1634 - [c169]Steven J. Krieg, Peter M. Kogge, Nitesh V. Chawla:
GrowHON: A Scalable Algorithm for Growing Higher-order Networks of Sequences. COMPLEX NETWORKS (2) 2020: 485-496 - [c168]Chuxu Zhang, Lu Yu, Mandana Saebi, Meng Jiang, Nitesh V. Chawla:
Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases. EMNLP (Findings) 2020: 580-585 - [c167]Priscilla Jiménez-Pazmino, Trenton Ford, Ronald A. Metoyer, Nitesh V. Chawla:
Identifying Bridge Users: the Knowledge Transfer Agents in Enterprise Collaboration Systems. HICSS 2020: 1-10 - [c166]Steven J. Krieg, Daniel H. Robertson, Meeta P. Pradhan, Nitesh V. Chawla:
Higher-order Networks of Diabetes Comorbidities: Disease Trajectories that Matter. ICHI 2020: 1-11 - [c165]Daheng Wang, Meng Jiang, Munira Syed, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla:
Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors. KDD 2020: 2581-2589 - [c164]Tina Eliassi-Rad, Nitesh V. Chawla, Vittoria Colizza, Lauren Gardner, Marcel Salathé, Samuel V. Scarpino, Joseph T. Wu:
Fighting a Pandemic: Convergence of Expertise, Data Science and Policy. KDD 2020: 3493-3494 - [c163]Chuxu Zhang, Meng Jiang, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla:
Multi-modal Network Representation Learning. KDD 2020: 3557-3558 - [c162]Suwen Lin, Xian Wu, Gonzalo J. Martínez, Nitesh V. Chawla:
Filling Missing Values on Wearable-Sensory Time Series Data. SDM 2020: 46-54 - [c161]Poorna Talkad Sukumar, Gonzalo J. Martínez, Ted Grover, Gloria Mark, Sidney K. D'Mello, Nitesh V. Chawla, Stephen M. Mattingly, Aaron D. Striegel:
Characterizing Exploratory Behaviors on a Personal Visualization Interface Using Interaction Logs. EuroVis (Short Papers) 2020: 79-83 - [c160]Xian Wu, Suleyman Cetintas, Deguang Kong, Miao Lu, Jian Yang, Nitesh V. Chawla:
Learning from Cross-Modal Behavior Dynamics with Graph-Regularized Neural Contextual Bandit. WWW 2020: 995-1005 - [c159]Xian Wu, Chao Huang, Chuxu Zhang, Nitesh V. Chawla:
Hierarchically Structured Transformer Networks for Fine-Grained Spatial Event Forecasting. WWW 2020: 2320-2330 - [e9]Carlotta Demeniconi, Nitesh V. Chawla:
Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020, Cincinnati, Ohio, USA, May 7-9, 2020. SIAM 2020, ISBN 978-1-61197-623-6 [contents]The conference was canceled because of the coronavirus pandemic, the reviewed papers are published in this volume. - [i48]Xian Wu, Chao Huang, Pablo Robles-Granda, Nitesh V. Chawla:
Representation Learning on Variable Length and Incomplete Wearable-Sensory Time Series. CoRR abs/2002.03595 (2020) - [i47]Mandana Saebi, Steven J. Krieg, Chuxu Zhang, Meng Jiang, Nitesh V. Chawla:
Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning. CoRR abs/2003.06050 (2020) - [i46]Steven J. Krieg, Jennifer J. Schnur, Jermaine D. Marshall, Matthew M. Schoenbauer, Nitesh V. Chawla:
Pandemic Pulse: Unraveling and Modeling Social Signals during the COVID-19 Pandemic. CoRR abs/2006.05983 (2020) - [i45]Daheng Wang, Meng Jiang, Munira Syed, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla:
Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors. CoRR abs/2006.06820 (2020) - [i44]Pablo Robles-Granda, Suwen Lin, Xian Wu, Sidney D'Mello, Gonzalo J. Martínez, Koustuv Saha, Kari Nies, Gloria Mark, Andrew T. Campbell, Munmun De Choudhury, Anind K. Dey, Julie M. Gregg, Ted Grover, Stephen M. Mattingly, Shayan Mirjafari, Edward Moskal, Aaron Striegel, Nitesh V. Chawla:
Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being. CoRR abs/2006.08364 (2020) - [i43]Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang:
Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network. CoRR abs/2006.09610 (2020) - [i42]Daheng Wang, Zhihan Zhang, Yihong Ma, Tong Zhao, Tianwen Jiang, Nitesh V. Chawla, Meng Jiang:
Learning Attribute-Structure Co-Evolutions in Dynamic Graphs. CoRR abs/2007.13004 (2020) - [i41]Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla:
AttrE2vec: Unsupervised Attributed Edge Representation Learning. CoRR abs/2012.14727 (2020)
2010 – 2019
- 2019
- [j66]Shuo Wang, Leandro L. Minku, Nitesh V. Chawla, Xin Yao:
Learning from data streams and class imbalance. Connect. Sci. 31(2): 103-104 (2019) - [j65]Shuo Wang, Leandro L. Minku, Nitesh V. Chawla, Xin Yao:
Learning in the presence of class imbalance and concept drift. Neurocomputing 343: 1-2 (2019) - [j64]Shayan Mirjafari, Kizito Masaba, Ted Grover, Weichen Wang, Pino G. Audia, Andrew T. Campbell, Nitesh V. Chawla, Vedant Das Swain, Munmun De Choudhury, Anind K. Dey, Sidney K. D'Mello, Ge Gao, Julie M. Gregg, Krithika Jagannath, Kaifeng Jiang, Suwen Lin, Qiang Liu, Gloria Mark, Gonzalo J. Martínez, Stephen M. Mattingly, Edward Moskal, Raghu Mulukutla, Subigya Nepal, Kari Nies, Manikanta D. Reddy, Pablo Robles-Granda, Koustuv Saha, Anusha Sirigiri, Aaron Striegel:
Differentiating Higher and Lower Job Performers in the Workplace Using Mobile Sensing. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(2): 37:1-37:24 (2019) - [j63]Vedant Das Swain, Koustuv Saha, Hemang Rajvanshy, Anusha Sirigiri, Julie M. Gregg, Suwen Lin, Gonzalo J. Martínez, Stephen M. Mattingly, Shayan Mirjafari, Raghu Mulukutla, Subigya Nepal, Kari Nies, Manikanta D. Reddy, Pablo Robles-Granda, Andrew T. Campbell, Nitesh V. Chawla, Sidney D'Mello, Anind K. Dey, Kaifeng Jiang, Qiang Liu, Gloria Mark, Edward Moskal, Aaron Striegel, Louis Tay, Gregory D. Abowd, Munmun De Choudhury:
A Multisensor Person-Centered Approach to Understand the Role of Daily Activities in Job Performance with Organizational Personas. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 3(4): 130:1-130:27 (2019) - [j62]Aastha Nigam, Reid A. Johnson, Dong Wang, Nitesh V. Chawla:
Characterizing online health and wellness information consumption: A study. Inf. Fusion 46: 33-43 (2019) - [j61]Shao-Yuan Li, Yuan Jiang, Nitesh V. Chawla, Zhi-Hua Zhou:
Multi-Label Learning from Crowds. IEEE Trans. Knowl. Data Eng. 31(7): 1369-1382 (2019) - [j60]Jun Tao, Martin Imre, Chaoli Wang, Nitesh V. Chawla, Hanqi Guo, Gokhan Sever, Seung Hyun Kim:
Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps. IEEE Trans. Vis. Comput. Graph. 25(1): 1236-1245 (2019) - [c158]Frederick Nwanganga, Nitesh V. Chawla:
Using Structural Similarity to Predict Future Workload Behavior in the Cloud. CLOUD 2019: 132-136 - [c157]Chuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang Feng, Cristian Lumezanu, Wei Cheng, Jingchao Ni, Bo Zong, Haifeng Chen, Nitesh V. Chawla:
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data. AAAI 2019: 1409-1416 - [c156]Koustuv Saha, Raghu Mulukutla, Kari Nies, Pablo Robles-Granda, Anusha Sirigiri, Dong Whi Yoo, Pino G. Audia, Andrew T. Campbell, Nitesh V. Chawla, Sidney K. D'Mello, Anind K. Dey, Manikanta D. Reddy, Kaifeng Jiang, Qiang Liu, Gloria Mark, Edward Moskal, Aaron Striegel, Munmun De Choudhury, Vedant Das Swain, Julie M. Gregg, Ted Grover, Suwen Lin, Gonzalo J. Martínez, Stephen M. Mattingly, Shayan Mirjafari:
Imputing Missing Social Media Data Stream in Multisensor Studies of Human Behavior. ACII 2019: 178-184 - [c155]Catherine Markley, Keith Feldman, Nitesh V. Chawla:
Outside the Hospital Walls: Associations of Value Based Care Metrics and Community Health Factors. BHI 2019: 1-4 - [c154]Tianwen Jiang, Zhihan Zhang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang:
CTGA: Graph-based Biomedical Literature Search. BIBM 2019: 395-400 - [c153]Stephen M. Mattingly, Julie M. Gregg, Pino G. Audia, Ayse Elvan Bayraktaroglu, Andrew T. Campbell, Nitesh V. Chawla, Vedant Das Swain, Munmun De Choudhury, Sidney K. D'Mello, Anind K. Dey, Ge Gao, Krithika Jagannath, Kaifeng Jiang, Suwen Lin, Qiang Liu, Gloria Mark, Gonzalo J. Martínez, Kizito Masaba, Shayan Mirjafari, Edward Moskal, Raghu Mulukutla, Kari Nies, Manikanta D. Reddy, Pablo Robles-Granda, Koustuv Saha, Anusha Sirigiri, Aaron Striegel:
The Tesserae Project: Large-Scale, Longitudinal, In Situ, Multimodal Sensing of Information Workers. CHI Extended Abstracts 2019 - [c152]Koustuv Saha, Ayse Elvan Bayraktaroglu, Andrew T. Campbell, Nitesh V. Chawla, Munmun De Choudhury, Sidney K. D'Mello, Anind K. Dey, Ge Gao, Julie M. Gregg, Krithika Jagannath, Gloria Mark, Gonzalo J. Martínez, Stephen M. Mattingly, Edward Moskal, Anusha Sirigiri, Aaron Striegel, Dong Whi Yoo:
Social Media as a Passive Sensor in Longitudinal Studies of Human Behavior and Wellbeing. CHI Extended Abstracts 2019 - [c151]Chao Huang, Baoxu Shi, Xuchao Zhang, Xian Wu, Nitesh V. Chawla:
Similarity-Aware Network Embedding with Self-Paced Learning. CIKM 2019: 2113-2116 - [c150]Chao Huang, Xian Wu, Xuchao Zhang, Suwen Lin, Nitesh V. Chawla:
Deep Prototypical Networks for Imbalanced Time Series Classification under Data Scarcity. CIKM 2019: 2141-2144 - [c149]Munira Syed, Jermaine Marshall, Aastha Nigam, Nitesh V. Chawla:
Gender Prediction Through Synthetic Resampling of User Profiles Using SeqGANs. CSoNet 2019: 363-370 - [c148]Beenish Moalla Chaudhry, Louis Faust, Nitesh V. Chawla:
Development and Evaluation of a Web Application for Prenatal Care Coordinators in the United States. DESRIST 2019: 140-156 - [c147]Munira Syed, Malolan Chetlur, Shazia Afzal, G. Alex Ambrose, Nitesh V. Chawla:
Implicit and Explicit Emotions in MOOCs. EDM 2019 - [c146]Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang:
Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text. EMNLP/IJCNLP (1) 2019: 302-312 - [c145]Frederick Nwanganga, Nitesh V. Chawla, Gregory R. Madey:
Statistical Analysis and Modeling of Heterogeneous Workloads on Amazon's Public Cloud Infrastructure. HICSS 2019: 1-10 - [c144]Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, Nitesh V. Chawla:
Heterogeneous Graph Neural Network. KDD 2019: 793-803 - [c143]Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang:
The Role of: A Novel Scientific Knowledge Graph Representation and Construction Model. KDD 2019: 1634-1642 - [c142]Daheng Wang, Tianwen Jiang, Nitesh V. Chawla, Meng Jiang:
TUBE: Embedding Behavior Outcomes for Predicting Success. KDD 2019: 1682-1690 - [c141]Chao Huang, Xian Wu, Xuchao Zhang, Chuxu Zhang, Jiashu Zhao, Dawei Yin, Nitesh V. Chawla:
Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics. KDD 2019: 2613-2622 - [c140]Munira Syed, Trunojoyo Anggara, Alison Lanski, Xiaojing Duan, G. Alex Ambrose, Nitesh V. Chawla:
Integrated Closed-loop Learning Analytics Scheme in a First Year Experience Course. LAK 2019: 521-530 - [c139]Beenish M. Chaudhry, Louis Faust, Nitesh V. Chawla:
From Design to Development to Evaluation of a Pregnancy App for Low-Income Women in a Community-Based Setting. MobileHCI 2019: 7:1-7:11 - [c138]Louis Faust, Priscilla Jiménez-Pazmino, James K. Holland, Omar Lizardo, David Hachen, Nitesh V. Chawla:
What 30 Days Tells Us About 3 Years: Identifying Early Signs of User Abandonment and Non-Adherence. PervasiveHealth 2019: 216-224 - [c137]Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Nitesh V. Chawla:
Neural Tensor Factorization for Temporal Interaction Learning. WSDM 2019: 537-545 - [c136]Chuxu Zhang, Ananthram Swami, Nitesh V. Chawla:
SHNE: Representation Learning for Semantic-Associated Heterogeneous Networks. WSDM 2019: 690-698 - [c135]Chao Huang, Chuxu Zhang, Jiashu Zhao, Xian Wu, Nitesh V. Chawla, Dawei Yin:
MiST: A Multiview and Multimodal Spatial-Temporal Learning Framework for Citywide Abnormal Event Forecasting. WWW 2019: 717-728 - [e8]Tanya Y. Berger-Wolf, Nitesh V. Chawla:
Proceedings of the 2019 SIAM International Conference on Data Mining, SDM 2019, Calgary, Alberta, Canada, May 2-4, 2019. SIAM 2019, ISBN 978-1-61197-567-3 [contents] - [i40]Tomasz Kajdanowicz, Kamil Tagowski, Maciej Falkiewicz, Piotr Bielak, Przemyslaw Kazienko, Nitesh V. Chawla:
Incremental embedding for temporal networks. CoRR abs/1904.03423 (2019) - [i39]Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang:
Constructing Information-Lossless Biological Knowledge Graphs from Conditional Statements. CoRR abs/1907.00720 (2019) - [i38]Mandana Saebi, Giovanni Luca Ciampaglia, Lance M. Kaplan, Nitesh V. Chawla:
HONEM: Network Embedding Using Higher-Order Patterns in Sequential Data. CoRR abs/1908.05387 (2019) - [i37]Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh V. Chawla, Zhenhui Li:
Graph Few-shot Learning via Knowledge Transfer. CoRR abs/1910.03053 (2019) - [i36]Chuxu Zhang, Huaxiu Yao, Chao Huang, Meng Jiang, Zhenhui Li, Nitesh V. Chawla:
Few-Shot Knowledge Graph Completion. CoRR abs/1911.11298 (2019) - 2018
- [j59]Pamela Bilo Thomas, Daniel H. Robertson, Nitesh V. Chawla:
Predicting onset of complications from diabetes: a graph based approach. Appl. Netw. Sci. 3(1): 48:1-48:16 (2018) - [j58]Saurabh Nagrecha, Reid A. Johnson, Nitesh V. Chawla:
FraudBuster: Reducing Fraud in an Auto Insurance Market. Big Data 6(1): 3-12 (2018) - [j57]Zheng Yan, Jun Liu, Laurence T. Yang, Nitesh V. Chawla:
Big data fusion in Internet of Things. Inf. Fusion 40: 32-33 (2018) - [j56]Alberto Fernández, Salvador García, Francisco Herrera, Nitesh V. Chawla:
SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary. J. Artif. Intell. Res. 61: 863-905 (2018) - [j55]Keith Feldman, Spyros Kotoulas, Nitesh V. Chawla:
TIQS: Targeted Iterative Question Selection for Health Interventions. J. Heal. Informatics Res. 2(3): 205-227 (2018) - [j54]Keith Feldman, Reid A. Johnson, Nitesh V. Chawla:
The State of Data in Healthcare: Path Towards Standardization. J. Heal. Informatics Res. 2(3): 248-271 (2018) - [j53]Hong Huang, Yuxiao Dong, Jie Tang, Hongxia Yang, Nitesh V. Chawla, Xiaoming Fu:
Will Triadic Closure Strengthen Ties in Social Networks? ACM Trans. Knowl. Discov. Data 12(3): 30:1-30:25 (2018) - [j52]Jun Tao, Chaoli Wang, Nitesh V. Chawla, Lei Shi, Seung Hyun Kim:
Semantic Flow Graph: A Framework for Discovering Object Relationships in Flow Fields. IEEE Trans. Vis. Comput. Graph. 24(12): 3200-3213 (2018) - [c134]Keith Feldman, Mayra Duarte, Waldo Mikels-Carrasco, Nitesh V. Chawla:
Leveraging health and wellness platforms to understand childhood obesity: A usability pilot of FitSpace. BHI 2018: 418-421 - [c133]Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Louis Faust, Nitesh V. Chawla:
RESTFul: Resolution-Aware Forecasting of Behavioral Time Series Data. CIKM 2018: 1073-1082 - [c132]Chao Huang, Junbo Zhang, Yu Zheng, Nitesh V. Chawla:
DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction. CIKM 2018: 1423-1432 - [c131]Nuno Moniz, Rita P. Ribeiro, Vítor Cerqueira, Nitesh V. Chawla:
SMOTEBoost for Regression: Improving the Prediction of Extreme Values. DSAA 2018: 150-159 - [c130]Louis Faust, David Hachen, Omar Lizardo, Nitesh V. Chawla:
Quantifying Subjective Well-Being Using Trends in Weekend Activity. ICHI 2018: 123-129 - [c129]Chuxu Zhang, Lu Yu, Xiangliang Zhang, Nitesh V. Chawla:
Task-Guided and Semantic-Aware Ranking for Academic Author-Paper Correlation Inference. IJCAI 2018: 3641-3647 - [c128]Qiyu Zhi, Suwen Lin, Shuai He, Ronald A. Metoyer, Nitesh V. Chawla:
VisPod: Content-Based Audio Visual Navigation. IUI Companion 2018: 10:1-10:2 - [c127]Daheng Wang, Meng Jiang, Qingkai Zeng, Zachary Eberhart, Nitesh V. Chawla:
Multi-Type Itemset Embedding for Learning Behavior Success. KDD 2018: 2397-2406 - [c126]Beenish Moalla Chaudhry, Louis Faust, Nitesh V. Chawla:
Towards an Integrated mHealth Platform for Community-based Maternity Health Workers in Low-Income Communities. PervasiveHealth 2018: 118-127 - [c125]Aastha Nigam, Kijung Shin, Ashwin Bahulkar, Bryan Hooi, David Hachen, Boleslaw K. Szymanski, Christos Faloutsos, Nitesh V. Chawla:
ONE-M: Modeling the Co-evolution of Opinions and Network Connections. ECML/PKDD (2) 2018: 122-140 - [c124]Xian Wu, Yuxiao Dong, Baoxu Shi, Ananthram Swami, Nitesh V. Chawla:
Who will Attend This Event Together? Event Attendance Prediction via Deep LSTM Networks. SDM 2018: 180-188 - [c123]Chuxu Zhang, Chao Huang, Lu Yu, Xiangliang Zhang, Nitesh V. Chawla:
Camel: Content-Aware and Meta-path Augmented Metric Learning for Author Identification. WWW 2018: 709-718 - [r2]Yang Yang, Nitesh V. Chawla:
Link Prediction: A Primer. Encyclopedia of Social Network Analysis and Mining. 2nd Ed. 2018 - [i35]Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Nitesh V. Chawla:
Neural Tensor Factorization. CoRR abs/1802.04416 (2018) - [i34]Louis Faust, Priscilla Jiménez, David Hachen, Omar Lizardo, Aaron Striegel, Nitesh V. Chawla:
Long-term Compliance Habits: What Early Data Tells Us. CoRR abs/1804.04256 (2018) - [i33]Chuxu Zhang, Ananthram Swami, Nitesh V. Chawla:
CARL: Content-Aware Representation Learning for Heterogeneous Networks. CoRR abs/1805.04983 (2018) - [i32]Suwen Lin, Louis Faust, Pablo Robles-Granda, Nitesh V. Chawla:
Social Network Structure is Predictive of Health and Wellness. CoRR abs/1809.00029 (2018) - [i31]Chuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang Feng, Cristian Lumezanu, Wei Cheng, Jingchao Ni, Bo Zong, Haifeng Chen, Nitesh V. Chawla:
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data. CoRR abs/1811.08055 (2018) - [i30]Piotr Szymanski, Tomasz Kajdanowicz, Nitesh V. Chawla:
LNEMLC: Label Network Embeddings for Multi-Label Classifiation. CoRR abs/1812.02956 (2018) - 2017
- [j51]Aastha Nigam, Henry K. Dambanemuya, Madhav Joshi, Nitesh V. Chawla:
Harvesting Social Signals to Inform Peace Processes Implementation and Monitoring. Big Data 5(4): 337-355 (2017) - [j50]Ashwin Bahulkar, Boleslaw K. Szymanski, Nitesh V. Chawla, Omar Lizardo, Kevin S. Chan:
Influence of Personal Preferences on Link Dynamics in Social Networks. Complex. 2017: 4543563:1-4543563:12 (2017) - [j49]Pablo González, Alberto Castaño, Nitesh V. Chawla, Juan José del Coz:
A Review on Quantification Learning. ACM Comput. Surv. 50(5): 74:1-74:40 (2017) - [j48]Md Mursalin, Yuan Zhang, Yuehui Chen, Nitesh V. Chawla:
Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier. Neurocomputing 241: 204-214 (2017) - [j47]Pablo González, Jorge Díez, Nitesh V. Chawla, Juan José del Coz:
Why is quantification an interesting learning problem? Prog. Artif. Intell. 6(1): 53-58 (2017) - [j46]Yuxiao Dong, Nitesh V. Chawla, Jie Tang, Yang Yang, Yang Yang:
User Modeling on Demographic Attributes in Big Mobile Social Networks. ACM Trans. Inf. Syst. 35(4): 35:1-35:33 (2017) - [c122]Frederick Nwanganga, Mandana Saebi, Gregory R. Madey, Nitesh V. Chawla:
A Minimum-Cost Flow Model for Workload Optimization on Cloud Infrastructure. CLOUD 2017: 480-487 - [c121]Shazia Afzal, Bikram Sengupta, Munira Syed, Nitesh V. Chawla, G. Alex Ambrose, Malolan Chetlur:
The ABC of MOOCs: Affect and its inter-play with behavior and cognition. ACII 2017: 279-284 - [c120]Jun Tao, Jian Xu, Chaoli Wang, Nitesh V. Chawla:
HoNVis: Visualizing and exploring higher-order networks. PacificVis 2017: 1-10 - [c119]Jian Xu, Nitesh V. Chawla:
Mining Features Associated with Effective Tweets. ASONAM 2017: 525-532 - [c118]Xian Wu, Yuxiao Dong, Jun Tao, Chao Huang, Nitesh V. Chawla:
Reliable fake review detection via modeling temporal and behavioral patterns. IEEE BigData 2017: 494-499 - [c117]Chuxu Zhang, Lu Yu, Xiangliang Zhang, Nitesh V. Chawla:
ImWalkMF: Joint matrix factorization and implicit walk integrative learning for recommendation. IEEE BigData 2017: 857-866 - [c116]Saurabh Nagrecha, Pamela Bilo Thomas, Keith Feldman, Nitesh V. Chawla:
Predicting Chronic Heart Failure Using Diagnoses Graphs. CD-MAKE 2017: 295-312 - [c115]Louis Faust, Rachael Purta, David Hachen, Aaron Striegel, Christian Poellabauer, Omar Lizardo, Nitesh V. Chawla:
Exploring Compliance: Observations from a Large Scale Fitbit Study. SocialSens@CPSWeek 2017: 55-60 - [c114]Pingjie Tang, Jed Pitera, Dmitry Zubarev, Nitesh V. Chawla:
Materials Science Literature-Patent Relevance Search: A Heterogeneous Network Analysis Approach. DSAA 2017: 146-154 - [c113]Jian Xu, Jun Tao, Nitesh V. Chawla, Chaoli Wang:
Visual Analytics of Higher-order Dependencies in Sensor Data: Demo Abstract. IoTDI 2017: 297-298 - [c112]Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami:
metapath2vec: Scalable Representation Learning for Heterogeneous Networks. KDD 2017: 135-144 - [c111]Yuxiao Dong, Reid A. Johnson, Jian Xu, Nitesh V. Chawla:
Structural Diversity and Homophily: A Study Across More Than One Hundred Big Networks. KDD 2017: 807-816 - [c110]Mehdi Golestanian, Christian Poellabauer, Nitesh V. Chawla:
Poster: RSSI-Based Pedestrian Localization Using Artificial Neural Networks. CarSys@MobiCom 2017: 79-80 - [c109]Xian Wu, Yuxiao Dong, Chao Huang, Jian Xu, Dong Wang, Nitesh V. Chawla:
UAPD: Predicting Urban Anomalies from Spatial-Temporal Data. ECML/PKDD (2) 2017: 622-638 - [c108]Saurabh Nagrecha, John Z. Dillon, Nitesh V. Chawla:
MOOC Dropout Prediction: Lessons Learned from Making Pipelines Interpretable. WWW (Companion Volume) 2017: 351-359 - [e7]Nitesh V. Chawla, Wei Wang:
Proceedings of the 2017 SIAM International Conference on Data Mining, Houston, Texas, USA, April 27-29, 2017. SIAM 2017, ISBN 978-1-61197-497-3 [contents] - [i29]Jun Tao, Jian Xu, Chaoli Wang, Nitesh V. Chawla:
HoNVis: Visualizing and Exploring Higher-Order Networks. CoRR abs/1702.00737 (2017) - [i28]Keith Feldman, Louis Faust, Xian Wu, Chao Huang, Nitesh V. Chawla:
Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline. CoRR abs/1706.01513 (2017) - [i27]Jian Xu, Nitesh V. Chawla:
Mining Features Associated with Effective Tweets. CoRR abs/1706.07484 (2017) - [i26]Frederick Nwanganga, Mandana Saebi, Gregory R. Madey, Nitesh V. Chawla:
A Minimum-Cost Flow Model for Workload Optimization on Cloud Infrastructure. CoRR abs/1707.09317 (2017) - [i25]Shuo Wang, Leandro L. Minku, Nitesh V. Chawla, Xin Yao:
Proceedings of the IJCAI 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17). CoRR abs/1707.09425 (2017) - [i24]Ashwin Bahulkar, Boleslaw K. Szymanski, Nitesh V. Chawla, Omar Lizardo, Kevin S. Chan:
Influence of Personal Preferences on Link Dynamics in Social Networks. CoRR abs/1709.07401 (2017) - [i23]Jian Xu, Mandana Saebi, Bruno Ribeiro, Lance M. Kaplan, Nitesh V. Chawla:
Detecting Anomalies in Sequential Data with Higher-order Networks. CoRR abs/1712.09658 (2017) - 2016
- [j45]Dipanwita Dasgupta, Beenish M. Chaudhry, Emily Koh, Nitesh V. Chawla:
A Survey of Tablet Applications for Promoting Successful Aging in Older Adults. IEEE Access 4: 9005-9017 (2016) - [j44]Yuan Zhang, Lin Zhang, Eiji Oki, Nitesh V. Chawla, Anton Kos:
Ieee Access Special Section Editorial: Big Data Analytics for Smart and Connected Health. IEEE Access 4: 9906-9909 (2016) - [j43]Saurabh Nagrecha, Nitesh V. Chawla:
Quantifying decision making for data science: from data acquisition to modeling. EPJ Data Sci. 5(1): 27 (2016) - [j42]Lawrence B. Holder, Rajmonda Sulo Caceres, David F. Gleich, E. Jason Riedy, Maleq Khan, Nitesh V. Chawla, Ravi Kumar, Yinghui Wu, Christine Klymko, Tina Eliassi-Rad, B. Aditya Prakash:
Current and Future Challenges in Mining Large Networks: Report on the Second SDM Workshop on Mining Networks and Graphs. SIGKDD Explor. 18(1): 39-45 (2016) - [j41]Yuxiao Dong, Reid A. Johnson, Nitesh V. Chawla:
Can Scientific Impact Be Predicted? IEEE Trans. Big Data 2(1): 18-30 (2016) - [j40]Haibo He, Nitesh V. Chawla, Huanhuan Chen, Yoonsuck Choe, Andries P. Engelbrecht, Jaya deva, Lyle N. Long, Ali A. Minai, Feiping Nie, Umut Ozertem, Barak A. Pearlmutter, Ling Shao, Jennie Si, Jochen J. Steil, Brijesh K. Verma, Ding Wang:
Editorial IEEE Transactions on Neural Networks and Learning Systems 2016 and Beyond. IEEE Trans. Neural Networks Learn. Syst. 27(1): 1-7 (2016) - [c107]Aastha Nigam, Nitesh V. Chawla:
Link Prediction in a Semi-bipartite Network for Recommendation. ACIIDS (2) 2016: 127-135 - [c106]Dipanwita Dasgupta, Reid A. Johnson, Beenish M. Chaudhry, Kimberly Green Reeves, Patty Willaert, Nitesh V. Chawla:
Design and Evaluation of a Medication Adherence Application with Communication for Seniors in Independent Living Communities. AMIA 2016 - [c105]Ashwin Bahulkar, Boleslaw K. Szymanski, Omar Lizardo, Yuxiao Dong, Yang Yang, Nitesh V. Chawla:
Analysis of link formation, persistence and dissolution in NetSense data. ASONAM 2016: 1197-1204 - [c104]Aastha Nigam, Salvador Aguiñaga, Nitesh V. Chawla:
Connecting the dots to infer followers' topical interest on Twitter. BESC 2016: 1-6 - [c103]Dipanwita Dasgupta, Nitesh V. Chawla:
MedCare: Leveraging Medication Similarity for Disease Prediction. DSAA 2016: 706-715 - [c102]Keith Feldman, Nicholas L. Hazekamp, Nitesh V. Chawla:
Mining the Clinical Narrative: All Text are Not Equal. ICHI 2016: 271-280 - [c101]Dipanwita Dasgupta, Kimberly Green Reeves, Beenish M. Chaudhry, Mayra Duarte, Nitesh V. Chawla:
eSeniorCare: Technology for Promoting Well-Being of Older Adults in Independent Living Facilities. ICHI 2016: 461-472 - [c100]Siddharth Pal, Yuxiao Dong, Bishal Thapa, Nitesh V. Chawla, Ananthram Swami, Ram Ramanathan:
Deep learning for network analysis: Problems, approaches and challenges. MILCOM 2016: 588-593 - [c99]Beenish M. Chaudhry, Kimberly Green Reeves, Nitesh V. Chawla:
Successful aging for low-income older adults: towards design principles. PervasiveHealth 2016: 109-113 - [c98]Beenish M. Chaudhry, Mayra Duarte, Nitesh V. Chawla, Dipanwita Dasgupta:
Developing health technologies for older adults: methodological and ethical considerations. PervasiveHealth 2016: 330-332 - [i22]Yuxiao Dong, Reid A. Johnson, Jian Xu, Nitesh V. Chawla:
Structural Diversity and Homophily: A Study Across More than One Hundred Large-Scale Networks. CoRR abs/1602.07048 (2016) - [i21]Yang Yang, Nitesh V. Chawla, Ryan N. Lichtenwalter, Yuxiao Dong:
Influence Activation Model: A New Perspective in Social Influence Analysis and Social Network Evolution. CoRR abs/1605.08410 (2016) - [i20]Yuxiao Dong, Reid A. Johnson, Nitesh V. Chawla:
Can Scientific Impact Be Predicted? CoRR abs/1606.05905 (2016) - [i19]Yuxiao Dong, Omar Lizardo, 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. CoRR abs/1606.07556 (2016) - [i18]Yang Yang, Omar Lizardo, Dong Wang, Yuxiao Dong, Aaron D. Striegel, David Hachen, Nitesh V. Chawla:
Gender Differences in Communication Behaviors, Spatial Proximity Patterns, and Mobility Habits. CoRR abs/1607.06740 (2016) - [i17]Ashwin Bahulkar, Boleslaw K. Szymanski, Omar Lizardo, Yuxiao Dong, Yang Yang, Nitesh V. Chawla:
Analysis of Link Formation, Persistence and Dissolution in NetSense Data. CoRR abs/1611.00568 (2016) - [i16]Geoffrey Siwo, Andrew K. Rider, Asako Tan, Richard S. Pinapati, Scott J. Emrich, Nitesh V. Chawla, Michael T. Ferdig:
Prediction of fine-tuned promoter activity from DNA sequence. F1000Research 5: 158 (2016) - 2015
- [j39]Keith Feldman, Nitesh V. Chawla:
Does Medical School Training Relate to Practice? Evidence from Big Data. Big Data 3(2): 103-113 (2015) - [j38]Keith Feldman, Darcy A. Davis, Nitesh V. Chawla:
Scaling and contextualizing personalized healthcare: A case study of disease prediction algorithm integration. J. Biomed. Informatics 57: 377-385 (2015) - [j37]Yang Yang, Ryan N. Lichtenwalter, Nitesh V. Chawla:
Evaluating link prediction methods. Knowl. Inf. Syst. 45(3): 751-782 (2015) - [c97]Saurabh Nagrecha, Nitesh V. Chawla, Horst Bunke:
Recurrent Subgraph Prediction. ASONAM 2015: 416-423 - [c96]Yuxiao Dong, Reid A. Johnson, Yang Yang, Nitesh V. Chawla:
Collaboration Signatures Reveal Scientific Impact. ASONAM 2015: 480-487 - [c95]Keith Feldman, Louis Faust, Xian Wu, Chao Huang, Nitesh V. Chawla:
Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline. BIRS-IMLKE 2015: 150-169 - [c94]Everaldo Aguiar, Saurabh Nagrecha, Nitesh V. Chawla:
Predicting online video engagement using clickstreams. DSAA 2015: 1-10 - [c93]Nitesh V. Chawla:
Being a "Dataologist": From Data to Networks to Personalized Healthcare. IFSA-EUSFLAT 2015 - [c92]Yuxiao Dong, Jing Zhang, Jie Tang, Nitesh V. Chawla, Bai Wang:
CoupledLP: Link Prediction in Coupled Networks. KDD 2015: 199-208 - [c91]Frederick Nwanganga, Everaldo Aguiar, G. Alex Ambrose, Victoria Goodrich, Nitesh V. Chawla:
Qualitatively exploring electronic portfolios: a text mining approach to measuring student emotion as an early warning indicator. LAK 2015: 422-423 - [c90]Chao Huang, Dong Wang, Nitesh V. Chawla:
Towards Time-Sensitive Truth Discovery in Social Sensing Applications. MASS 2015: 154-162 - [c89]Reid A. Johnson, Troy Raeder, Nitesh V. Chawla:
Optimizing Classifiers for Hypothetical Scenarios. PAKDD (1) 2015: 264-276 - [c88]Yuxiao Dong, Nitesh V. Chawla, Jie Tang, Yang Yang, Yang Yang:
The Evolution of Social Relationships and Strategies Across the Lifespan. ECML/PKDD (3) 2015: 245-249 - [c87]Yuxiao Dong, Reid A. Johnson, Nitesh V. Chawla:
Will This Paper Increase Your h-index? ECML/PKDD (3) 2015: 259-263 - [c86]Yuxiao Dong, Fabio Pinelli, Yiannis Gkoufas, Zubair Nabi, Francesco Calabrese, Nitesh V. Chawla:
Inferring Unusual Crowd Events from Mobile Phone Call Detail Records. ECML/PKDD (2) 2015: 474-492 - [c85]Chao Huang, Dong Wang, Nitesh V. Chawla:
On spatial-temporal truth finding in social sensing. SECON 2015: 151-153 - [c84]Yuxiao Dong, Reid A. Johnson, Nitesh V. Chawla:
Will This Paper Increase Your h-index?: Scientific Impact Prediction. WSDM 2015: 149-158 - [e6]Przemyslaw Kazienko, Nitesh V. Chawla:
Applications of Social Media and Social Network Analysis. Lecture Notes in Social Networks, Springer 2015, ISBN 978-3-319-19002-0 [contents] - [i15]Yuxiao Dong, Fabio Pinelli, Yiannis Gkoufas, Zubair Nabi, Francesco Calabrese, Nitesh V. Chawla:
Inferring Unusual Crowd Events From Mobile Phone Call Detail Records. CoRR abs/1504.03643 (2015) - [i14]Yang Yang, Ryan N. Lichtenwalter, Nitesh V. Chawla:
Evaluating Link Prediction Methods. CoRR abs/1505.04094 (2015) - [i13]Jian Xu, Thanuka L. Wickramarathne, Nitesh V. Chawla:
Representing Higher Order Dependencies in Networks. CoRR abs/1508.03113 (2015) - [i12]Yang Yang, Jie Tang, Yuxiao Dong, Qiaozhu Mei, Reid A. Johnson, Nitesh V. Chawla:
Modeling the Interplay Between Individual Behavior and Network Distributions. CoRR abs/1511.02562 (2015) - 2014
- [j36]Andrew K. Rider, Geoffrey Siwo, Scott J. Emrich, Michael T. Ferdig, Nitesh V. Chawla:
A supervised learning approach to the ensemble clustering of genes. Int. J. Data Min. Bioinform. 9(2): 199-219 (2014) - [j35]Thanuka L. Wickramarathne, Kamal Premaratne, Manohar N. Murthi, Nitesh V. Chawla:
Convergence Analysis of Iterated Belief Revision in Complex Fusion Environments. IEEE J. Sel. Top. Signal Process. 8(4): 598-612 (2014) - [j34]Everaldo Aguiar, G. Alex Ambrose, Nitesh V. Chawla, Victoria Goodrich, Jay B. Brockman:
Engagement vs Performance: Using Electronic Portfolios to Predict First Semester Engineering Student Persistence. J. Learn. Anal. 1(3): 7-33 (2014) - [c83]Dipanwita Dasgupta, Keith Feldman, Disha Waghray, W. A. Mikels-Carrasco, Patty Willaert, Debra A. Raybold, Nitesh V. Chawla:
An integrated and digitized care framework for successful aging. BHI 2014: 440-443 - [c82]Keith Feldman, Nitesh V. Chawla:
Admission duration model for infant treatment (ADMIT). BIBM 2014: 583-587 - [c81]Andrea Dal Pozzolo, Reid A. Johnson, Olivier Caelen, Serge Waterschoot, Nitesh V. Chawla, Gianluca Bontempi:
Using HDDT to avoid instances propagation in unbalanced and evolving data streams. IJCNN 2014: 588-594 - [c80]Yuxiao Dong, Yang Yang, Jie Tang, Yang Yang, Nitesh V. Chawla:
Inferring user demographics and social strategies in mobile social networks. KDD 2014: 15-24 - [c79]Jian Xu, Thanuka L. Wickramarathne, Nitesh V. Chawla, Erin K. Grey, Karsten Steinhaeuser, Reuben P. Keller, John M. Drake, David M. Lodge:
Improving management of aquatic invasions by integrating shipping network, ecological, and environmental data: data mining for social good. KDD 2014: 1699-1708 - [c78]Everaldo Aguiar, Nitesh V. Chawla, Jay B. Brockman, G. Alex Ambrose, Victoria Goodrich:
Engagement vs performance: using electronic portfolios to predict first semester engineering student retention. LAK 2014: 103-112 - [r1]Nitesh V. Chawla, Yang Yang:
Link Prediction: A Primer. Encyclopedia of Social Network Analysis and Mining 2014: 813-820 - [i11]Jian Xu, Thanuka L. Wickramarathne, Erin K. Grey, Karsten Steinhaeuser, Reuben P. Keller, John M. Drake, Nitesh V. Chawla, David M. Lodge:
Patterns of Ship-borne Species Spread: A Clustering Approach for Risk Assessment and Management of Non-indigenous Species Spread. CoRR abs/1401.5407 (2014) - [i10]Yuxiao Dong, Jie Tang, Nitesh V. Chawla, Tiancheng Lou, Yang Yang, Bai Wang:
Inferring social status and rich club effects in enterprise communication networks. CoRR abs/1404.3708 (2014) - [i9]Everaldo Aguiar, Saurabh Nagrecha, Nitesh V. Chawla:
Predicting Online Video Engagement Using Clickstreams. CoRR abs/1405.5147 (2014) - [i8]Yang Yang, Yuxiao Dong, Nitesh V. Chawla:
Predicting Node Degree Centrality with the Node Prominence Profile. CoRR abs/1412.2269 (2014) - [i7]Yuxiao Dong, Reid A. Johnson, Nitesh V. Chawla:
Will This Paper Increase Your h-index? Scientific Impact Prediction. CoRR abs/1412.4754 (2014) - 2013
- [j33]Ashok N. Srivastava, Nitesh V. Chawla:
Special Issue on CIDU '11. Stat. Anal. Data Min. 6(3): 157 (2013) - [j32]Darcy A. Davis, Ryan Lichtenwalter, Nitesh V. Chawla:
Supervised methods for multi-relational link prediction. Soc. Netw. Anal. Min. 3(2): 127-141 (2013) - [j31]T. Ryan Hoens, Marina Blanton, Aaron Steele, Nitesh V. Chawla:
Reliable medical recommendation systems with patient privacy. ACM Trans. Intell. Syst. Technol. 4(4): 67:1-67:31 (2013) - [c77]Saurabh Nagrecha, Pawan Lingras, Nitesh V. Chawla:
Comparison of Gene Co-expression Networks and Bayesian Networks. ACIIDS (1) 2013: 507-516 - [c76]Yang Yang, Nitesh V. Chawla, Prithwish Basu, Bhaskar Prabhala, Thomas La Porta:
Link prediction in human mobility networks. ASONAM 2013: 380-387 - [c75]Andrew K. Rider, Nitesh V. Chawla:
An Ensemble Topic Model for Sharing Healthcare Data and Predicting Disease Risk. BCB 2013: 333 - [c74]Andrew K. Rider, Reid A. Johnson, Darcy A. Davis, T. Ryan Hoens, Nitesh V. Chawla:
Classifier Evaluation with Missing Negative Class Labels. IDA 2013: 380-391 - [c73]Saurav Pandit, Jonathan Koch, Yang Yang, Brian Uzzi, Nitesh V. Chawla:
Red Black Network: Temporal and Topological Analysis of Two Intertwined Social Networks. MILCOM 2013: 719-724 - [c72]Yang Yang, Nitesh V. Chawla, Xiaohui Lu, Sibel Adali:
Prominence in networks: A co-evolving process. NSW 2013: 58-65 - [c71]Yuxiao Dong, Jie Tang, Tiancheng Lou, Bin Wu, Nitesh V. Chawla:
How Long Will She Call Me? Distribution, Social Theory and Duration Prediction. ECML/PKDD (2) 2013: 16-31 - [p4]Yang Yang, Yizhou Sun, Saurav Pandit, Nitesh V. Chawla, Jiawei Han:
Perspective on Measurement Metrics for Community Detection Algorithms. Mining Social Networks and Security Informatics 2013: 227-242 - [i6]Yang Yang, Yuxiao Dong, Nitesh V. Chawla:
Microscopic Evolution of Social Networks by Triad Position Profile. CoRR abs/1310.1525 (2013) - 2012
- [j30]David A. Cieslak, T. Ryan Hoens, Nitesh V. Chawla, W. Philip Kegelmeyer:
Hellinger distance decision trees are robust and skew-insensitive. Data Min. Knowl. Discov. 24(1): 136-158 (2012) - [j29]T. Ryan Hoens, Robi Polikar, Nitesh V. Chawla:
Learning from streaming data with concept drift and imbalance: an overview. Prog. Artif. Intell. 1(1): 89-101 (2012) - [j28]Jose G. Moreno-Torres, Troy Raeder, Rocío Alaíz-Rodríguez, Nitesh V. Chawla, Francisco Herrera:
A unifying view on dataset shift in classification. Pattern Recognit. 45(1): 521-530 (2012) - [c70]Ryan Lichtenwalter, Nitesh V. Chawla:
Link Prediction: Fair and Effective Evaluation. ASONAM 2012: 376-383 - [c69]Nitesh V. Chawla, Ashok N. Srivastava:
Foreword. CIDU 2012 - [c68]Reid A. Johnson, Nitesh V. Chawla, Jessica J. Hellmann:
Species distribution modeling and prediction: A class imbalance problem. CIDU 2012: 9-16 - [c67]Yuxiao Dong, Jie Tang, Sen Wu, Jilei Tian, Nitesh V. Chawla, Jinghai Rao, Huanhuan Cao:
Link Prediction and Recommendation across Heterogeneous Social Networks. ICDM 2012: 181-190 - [c66]Saurav Pandit, Yang Yang, Nitesh V. Chawla:
Maximizing Information Spread through Influence Structures in Social Networks. ICDM Workshops 2012: 258-265 - [c65]Yang Yang, Nitesh V. Chawla, Yizhou Sun, Jiawei Han:
Predicting Links in Multi-relational and Heterogeneous Networks. ICDM 2012: 755-764 - [c64]Thomas Ryan Hoens, Nitesh V. Chawla:
Learning in non-stationary environments with class imbalance. KDD 2012: 168-176 - [c63]Reid A. Johnson, Yang Yang, Everaldo Aguiar, Andrew K. Rider, Nitesh V. Chawla:
ALIVE: A Multi-relational Link Prediction Environment for the Healthcare Domain. PAKDD Workshops 2012: 36-46 - [c62]T. Ryan Hoens, Qi Qian, Nitesh V. Chawla, Zhi-Hua Zhou:
Building Decision Trees for the Multi-class Imbalance Problem. PAKDD (1) 2012: 122-134 - [c61]Yizhou Sun, Jiawei Han, Charu C. Aggarwal, Nitesh V. Chawla:
When will it happen?: relationship prediction in heterogeneous information networks. WSDM 2012: 663-672 - [c60]Ryan Lichtenwalter, Nitesh V. Chawla:
Vertex collocation profiles: subgraph counting for link analysis and prediction. WWW 2012: 1019-1028 - 2011
- [j27]Ryan Lichtenwalter, Nitesh V. Chawla:
LPmade: Link Prediction Made Easy. J. Mach. Learn. Res. 12: 2489-2492 (2011) - [j26]Ashok N. Srivastava, Nitesh V. Chawla:
Special issue on the best papers of the Conference on Intelligent Data Understanding (CIDU 2010). Stat. Anal. Data Min. 4(4): 355-357 (2011) - [j25]Karsten Steinhaeuser, Nitesh V. Chawla, Auroop R. Ganguly:
Complex networks as a unified framework for descriptive analysis and predictive modeling in climate science. Stat. Anal. Data Min. 4(5): 497-511 (2011) - [j24]Troy Raeder, Nitesh V. Chawla:
Market basket analysis with networks. Soc. Netw. Anal. Min. 1(2): 97-113 (2011) - [j23]Troy Raeder, Omar Lizardo, David Hachen, Nitesh V. Chawla:
Predictors of short-term decay of cell phone contacts in a large scale communication network. Soc. Networks 33(4): 245-257 (2011) - [c59]Ryan Lichtenwalter, Nitesh V. Chawla:
DisNet: A Framework for Distributed Graph Computation. ASONAM 2011: 263-270 - [c58]Darcy A. Davis, Ryan Lichtenwalter, Nitesh V. Chawla:
Multi-relational Link Prediction in Heterogeneous Information Networks. ASONAM 2011: 281-288 - [c57]Yang Yang, Yizhou Sun, Saurav Pandit, Nitesh V. Chawla, Jiawei Han:
Is Objective Function the Silver Bullet? A Case Study of Community Detection Algorithms on Social Networks. ASONAM 2011: 394-397 - [c56]Alex Pelan, Karsten Steinhaeuser, Nitesh V. Chawla, Dilkushi A. de Alwis Pitts, Auroop R. Ganguly:
Empirical comparison of correlation measures and pruning levels in complex networks representing the global climate system. CIDM 2011: 239-245 - [c55]Jake T. Lussier, Nitesh V. Chawla:
Network Effects on Tweeting. Discovery Science 2011: 209-220 - [c54]T. Ryan Hoens, Nitesh V. Chawla, Robi Polikar:
Heuristic Updatable Weighted Random Subspaces for Non-stationary Environments. ICDM 2011: 241-250 - [c53]Karsten Steinhaeuser, Nitesh V. Chawla, Auroop R. Ganguly:
Comparing Predictive Power in Climate Data: Clustering Matters. SSTD 2011: 39-55 - [e5]Ashok N. Srivastava, Nitesh V. Chawla, Amal Shehan Perera:
Proceedings of the 2011 Conference on Intelligent Data Understanding, CIDU 2011, October 19-21, 2011, Mountain View, California, USA. NASA Ames Research Center 2011 [contents] - [i5]Troy Raeder, Omar Lizardo, David Hachen, Nitesh V. Chawla:
Predictors of short-term decay of cell phone contacts in a large scale communication network. CoRR abs/1102.1753 (2011) - [i4]Kevin W. Bowyer, Nitesh V. Chawla, Lawrence O. Hall, W. Philip Kegelmeyer:
SMOTE: Synthetic Minority Over-sampling Technique. CoRR abs/1106.1813 (2011) - [i3]Nitesh V. Chawla, David Hachen, Omar Lizardo, Zoltán Toroczkai, Anthony Strathman, Cheng Wang:
Weighted reciprocity in human communication networks. CoRR abs/1108.2822 (2011) - [i2]Nitesh V. Chawla, Grigoris I. Karakoulas:
Learning From Labeled And Unlabeled Data: An Empirical Study Across Techniques And Domains. CoRR abs/1109.2047 (2011) - [i1]Mária Ercsey-Ravasz, Ryan Lichtenwalter, Nitesh V. Chawla, Zoltán Toroczkai:
Range-limited Centrality Measures in Complex Networks. CoRR abs/1111.5382 (2011) - 2010
- [j22]Andrew K. Rider, Geoffrey Siwo, Nitesh V. Chawla, Michael T. Ferdig, Scott J. Emrich:
A statistical approach to finding overlooked genetic associations. BMC Bioinform. 11: 526 (2010) - [j21]Darcy A. Davis, Nitesh V. Chawla, Nicholas A. Christakis, Albert-László Barabási:
Time to CARE: a collaborative engine for practical disease prediction. Data Min. Knowl. Discov. 20(3): 388-415 (2010) - [j20]Ryan Lichtenwalter, Katerina Lichtenwalter, Nitesh V. Chawla:
A Machine-Learning Approach to Autonomous Music Composition. J. Intell. Syst. 19(2): 95-124 (2010) - [j19]Karsten Steinhaeuser, Nitesh V. Chawla:
Identifying and evaluating community structure in complex networks. Pattern Recognit. Lett. 31(5): 413-421 (2010) - [j18]Karsten Steinhaeuser, Nitesh V. Chawla, Auroop R. Ganguly:
An exploration of climate data using complex networks. SIGKDD Explor. 12(1): 25-32 (2010) - [j17]Varun Chandola, Olufemi A. Omitaomu, Auroop R. Ganguly, Ranga Raju Vatsavai, Nitesh V. Chawla, João Gama, Mohamed Medhat Gaber:
Knowledge discovery from sensor data (SensorKDD). SIGKDD Explor. 12(2): 50-53 (2010) - [c52]Andrew K. Rider, Geoffrey Siwo, Scott J. Emrich, Michael T. Ferdig, Nitesh V. Chawla:
A supervised learning approach to the unsupervised clustering of genes. BIBM 2010: 322-328 - [c51]Karsten Steinhaeuser, Nitesh V. Chawla, Auroop R. Ganguly:
Complex Networks In Climate Science: Progress, Opportunities And Challenges. CIDU 2010: 16-26 - [c50]Jake T. Lussier, Troy Raeder, Nitesh V. Chawla:
Digging up the Dirt on User Generated Content Consumption. COMAD 2010: 155 - [c49]Troy Raeder, T. Ryan Hoens, Nitesh V. Chawla:
Consequences of Variability in Classifier Performance Estimates. ICDM 2010: 421-430 - [c48]Gregory Ditzler, Robi Polikar, Nitesh V. Chawla:
An Incremental Learning Algorithm for Non-stationary Environments and Class Imbalance. ICPR 2010: 2997-3000 - [c47]T. Ryan Hoens, Marina Blanton, Nitesh V. Chawla:
Reliable medical recommendation systems with patient privacy. IHI 2010: 173-182 - [c46]Brian Dentino, Darcy A. Davis, Nitesh V. Chawla:
HealthCareND: leveraging EHR and CARE for prospective healthcare. IHI 2010: 841-844 - [c45]Ryan Lichtenwalter, Jake T. Lussier, Nitesh V. Chawla:
New perspectives and methods in link prediction. KDD 2010: 243-252 - [c44]Troy Raeder, Marina Blanton, Nitesh V. Chawla, Keith B. Frikken:
Privacy-Preserving Network Aggregation. PAKDD (1) 2010: 198-207 - [c43]T. Ryan Hoens, Nitesh V. Chawla:
Generating Diverse Ensembles to Counter the Problem of Class Imbalance. PAKDD (2) 2010: 488-499 - [c42]Jake T. Lussier, Troy Raeder, Nitesh V. Chawla:
User Generated Content Consumption and Social Networking in Knowledge-Sharing OSNs. SBP 2010: 228-237 - [c41]Wei Liu, Sanjay Chawla, David A. Cieslak, Nitesh V. Chawla:
A Robust Decision Tree Algorithm for Imbalanced Data Sets. SDM 2010: 766-777 - [c40]T. Ryan Hoens, Marina Blanton, Nitesh V. Chawla:
A Private and Reliable Recommendation System for Social Networks. SocialCom/PASSAT 2010: 816-825 - [c39]Qi Liao, Aaron Striegel, Nitesh V. Chawla:
Visualizing graph dynamics and similarity for enterprise network security and management. VizSEC 2010: 34-45 - [p3]Nitesh V. Chawla:
Data Mining for Imbalanced Datasets: An Overview. Data Mining and Knowledge Discovery Handbook 2010: 875-886 - [e4]Ashok N. Srivastava, Nitesh V. Chawla, Philip S. Yu, Paul Melby:
Proceedings of the 2010 Conference on Intelligent Data Understanding, CIDU 2010, October 5-6, 2010, Mountain View, California, USA. NASA Ames Research Center 2010 [contents] - [e3]Mohamed Medhat Gaber, Ranga Raju Vatsavai, Olufemi A. Omitaomu, João Gama, Nitesh V. Chawla, Auroop R. Ganguly:
Knowledge Discovery from Sensor Data, Second International Workshop, Sensor-KDD 2008, Las Vegas, NV, USA, August 24-27, 2008, Revised Selected Papers. Lecture Notes in Computer Science 5840, Springer 2010, ISBN 978-3-642-12518-8 [contents] - [e2]Thanaruk Theeramunkong, Cholwich Nattee, Paulo J. L. Adeodato, Nitesh V. Chawla, Peter Christen, Philippe Lenca, Josiah Poon, Graham J. Williams:
New Frontiers in Applied Data Mining, PAKDD 2009 International Workshops, Bangkok, Thailand, April 27-30, 2009. Revised Selected Papers. Lecture Notes in Computer Science 5669, Springer 2010, ISBN 978-3-642-14639-8 [contents]
2000 – 2009
- 2009
- [j16]Laritza M. Taft, R. Scott Evans, Chi-Ren Shyu, Marlene J. Egger, Nitesh V. Chawla, Joyce A. Mitchell, Sidney N. Thornton, Bruce E. Bray, Michael W. Varner:
Countering imbalanced datasets to improve adverse drug event predictive models in labor and delivery. J. Biomed. Informatics 42(2): 356-364 (2009) - [j15]Troy Raeder, Nitesh V. Chawla:
Model Monitor (M2): Evaluating, Comparing, and Monitoring Models. J. Mach. Learn. Res. 10: 1387-1390 (2009) - [j14]David A. Cieslak, Nitesh V. Chawla:
A framework for monitoring classifiers' performance: when and why failure occurs? Knowl. Inf. Syst. 18(1): 83-108 (2009) - [j13]Olufemi A. Omitaomu, Ranga Raju Vatsavai, Auroop R. Ganguly, Nitesh V. Chawla, João Gama, Mohamed Medhat Gaber:
Knowledge discovery from sensor data (SensorKDD). SIGKDD Explor. 11(2): 84-87 (2009) - [j12]Yuchun Tang, Yan-Qing Zhang, Nitesh V. Chawla, Sven Krasser:
SVMs Modeling for Highly Imbalanced Classification. IEEE Trans. Syst. Man Cybern. Part B 39(1): 281-288 (2009) - [c38]Troy Raeder, Nitesh V. Chawla:
Modeling a Store's Product Space as a Social Network. ASONAM 2009: 164-169 - [c37]Faruck Morcos, Charles Lamanna, Nitesh V. Chawla, Jesús A. Izaguirre:
Determination of Specificity Residues in Two Component Systems using Graphlets. BIOCOMP 2009: 860-866 - [c36]Ryan Lichtenwalter, Katerina Lichtenwalter, Nitesh V. Chawla:
Applying Learning Algorithms to Music Generation. IICAI 2009: 483-502 - [c35]Karsten Steinhaeuser, Nitesh V. Chawla, Auroop R. Ganguly:
An exploration of climate data using complex networks. KDD Workshop on Knowledge Discovery from Sensor Data 2009: 23-31 - [c34]Sean McRoskey, James Notwell, Nitesh V. Chawla, Christian Poellabauer:
Mining in a mobile environment. KDD Workshop on Knowledge Discovery from Sensor Data 2009: 56-60 - [c33]Ryan Lichtenwalter, Nitesh V. Chawla:
Adaptive Methods for Classification in Arbitrarily Imbalanced and Drifting Data Streams. PAKDD Workshops 2009: 53-75 - [e1]Olufemi A. Omitaomu, Auroop R. Ganguly, João Gama, Ranga Raju Vatsavai, Nitesh V. Chawla, Mohamed Medhat Gaber:
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data, Paris, France, June 28, 2009. ACM 2009, ISBN 978-1-60558-668-7 [contents] - 2008
- [j11]Nitesh V. Chawla, David A. Cieslak, Lawrence O. Hall, Ajay Joshi:
Automatically countering imbalance and its empirical relationship to cost. Data Min. Knowl. Discov. 17(2): 225-252 (2008) - [j10]Qi Liao, David A. Cieslak, Aaron Striegel, Nitesh V. Chawla:
Using selective, short-term memory to improve resilience against DDoS exhaustion attacks. Secur. Commun. Networks 1(4): 287-299 (2008) - [j9]Ranga Raju Vatsavai, Olufemi A. Omitaomu, João Gama, Nitesh V. Chawla, Mohamed Medhat Gaber, Auroop R. Ganguly:
Knowledge discovery from sensor data (SensorKDD). SIGKDD Explor. 10(2): 68-73 (2008) - [c32]Darcy A. Davis, Nitesh V. Chawla, Nicholas Blumm, Nicholas A. Christakis, Albert-László Barabási:
Predicting individual disease risk based on medical history. CIKM 2008: 769-778 - [c31]Douglas Thain, Christopher Moretti, Hoang Bui, Li Yu, Nitesh V. Chawla, Patrick J. Flynn:
Using Small Abstractions to Program Large Distributed Systems. eScience 2008: 723-724 - [c30]David A. Cieslak, Nitesh V. Chawla, Douglas Thain:
Troubleshooting thousands of jobs on production grids using data mining techniques. GRID 2008: 217-224 - [c29]David A. Cieslak, Nitesh V. Chawla:
Start Globally, Optimize Locally, Predict Globally: Improving Performance on Imbalanced Data. ICDM 2008: 143-152 - [c28]Christopher Moretti, Karsten Steinhaeuser, Douglas Thain, Nitesh V. Chawla:
Scaling up Classifiers to Cloud Computers. ICDM 2008: 472-481 - [c27]Nitesh V. Chawla, Douglas Thain, Ryan Lichtenwalter, David A. Cieslak:
Data mining on the grid for the grid. IPDPS 2008: 1-5 - [c26]David A. Cieslak, Nitesh V. Chawla:
Analyzing PETs on Imbalanced Datasets When Training and Testing Class Distributions Differ. PAKDD 2008: 519-526 - [c25]David A. Cieslak, Nitesh V. Chawla:
Learning Decision Trees for Unbalanced Data. ECML/PKDD (1) 2008: 241-256 - 2007
- [j8]Alec Pawling, Nitesh V. Chawla, Gregory R. Madey:
Anomaly detection in a mobile communication network. Comput. Math. Organ. Theory 13(4): 407-422 (2007) - [c24]Nitesh V. Chawla, Kevin W. Bowyer:
Actively Exploring Creation of Face Space(s) for Improved Face Recognition. AAAI 2007: 809-814 - [c23]Tanu Malik, Randal C. Burns, Nitesh V. Chawla:
A Black-Box Approach to Query Cardinality Estimation. CIDR 2007: 56-67 - [c22]Gregory R. Madey, Albert-László Barabási, Nitesh V. Chawla, Marta C. González, David Hachen, Brett Lantz, Alec Pawling, Timothy W. Schoenharl, Gábor Szabó, Pu Wang, Ping Yan:
Enhanced Situational Awareness: Application of DDDAS Concepts to Emergency and Disaster Management. International Conference on Computational Science (1) 2007: 1090-1097 - [c21]David A. Cieslak, Nitesh V. Chawla:
Detecting Fractures in Classifier Performance. ICDM 2007: 123-132 - [c20]Nitesh V. Chawla, Jared Sylvester:
Exploiting Diversity in Ensembles: Improving the Performance on Unbalanced Datasets. MCS 2007: 397-406 - [c19]Michael J. Chapple, Nitesh V. Chawla, Aaron Striegel:
Authentication anomaly detection: a case study on a virtual private network. MineNet 2007: 17-22 - 2006
- [j7]Yang Liu, Nitesh V. Chawla, Mary P. Harper, Elizabeth Shriberg, Andreas Stolcke:
A study in machine learning from imbalanced data for sentence boundary detection in speech. Comput. Speech Lang. 20(4): 468-494 (2006) - [c18]David A. Cieslak, Nitesh V. Chawla, Aaron Striegel:
Combating imbalance in network intrusion datasets. GrC 2006: 732-737 - [c17]David A. Cieslak, Douglas Thain, Nitesh V. Chawla:
Troubleshooting Distributed Systems via Data Mining. HPDC 2006: 309-312 - [c16]Jared Sylvester, Nitesh V. Chawla:
Evolutionary Ensemble Creation and Thinning. IJCNN 2006: 5148-5155 - [c15]Alec Pawling, Nitesh V. Chawla, Amitabh Chaudhary:
Evaluation of Summarization Schemes for Learning in Streams. PKDD 2006: 347-358 - [c14]Tanu Malik, Randal C. Burns, Nitesh V. Chawla, Alexander S. Szalay:
Data management and query - Estimating query result sizes for proxy caching in scientific database federations. SC 2006: 102 - [p2]Danny Roobaert, Grigoris I. Karakoulas, Nitesh V. Chawla:
Information Gain, Correlation and Support Vector Machines. Feature Extraction 2006: 463-470 - 2005
- [j6]Nitesh V. Chawla, Grigoris I. Karakoulas:
Learning From Labeled And Unlabeled Data: An Empirical Study Across Techniques And Domains. J. Artif. Intell. Res. 23: 331-366 (2005) - [c13]Nitesh V. Chawla, Kevin W. Bowyer:
Random Subspaces and Subsampling for 2-D Face Recognition. CVPR (2) 2005: 582-589 - [c12]Nitesh V. Chawla, Kevin W. Bowyer:
Designing Multiple Classifier Systems for Face Recognition. Multiple Classifier Systems 2005: 407-416 - [c11]Nitesh V. Chawla:
Many Are Better Than One: Improving Probabilistic Estimates from Decision Trees. MLCW 2005: 41-55 - [c10]Nitesh V. Chawla, Kevin W. Bowyer:
Ensembles in face recognition: tackling the extremes of high dimensionality, temporality, and variance in data. SMC 2005: 2346-2351 - [p1]Nitesh V. Chawla:
Data Mining for Imbalanced Datasets: An Overview. The Data Mining and Knowledge Discovery Handbook 2005: 853-867 - 2004
- [j5]Predrag Radivojac, Nitesh V. Chawla, A. Keith Dunker, Zoran Obradovic:
Classification and knowledge discovery in protein databases. J. Biomed. Informatics 37(4): 224-239 (2004) - [j4]Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer:
Learning Ensembles from Bites: A Scalable and Accurate Approach. J. Mach. Learn. Res. 5: 421-451 (2004) - [j3]Nitesh V. Chawla, Nathalie Japkowicz, Aleksander Kotcz:
Editorial: special issue on learning from imbalanced data sets. SIGKDD Explor. 6(1): 1-6 (2004) - 2003
- [j2]Nitesh V. Chawla, Thomas E. Moore, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer, Clayton Springer:
Distributed learning with bagging-like performance. Pattern Recognit. Lett. 24(1-3): 455-471 (2003) - [c9]Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, Kevin W. Bowyer:
SMOTEBoost: Improving Prediction of the Minority Class in Boosting. PKDD 2003: 107-119 - 2002
- [j1]Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, W. Philip Kegelmeyer:
SMOTE: Synthetic Minority Over-sampling Technique. J. Artif. Intell. Res. 16: 321-357 (2002) - [c8]Steven Eschrich, Nitesh V. Chawla, Lawrence O. Hall:
Generalization Methods in Bioinformatics. BIOKDD 2002: 25-32 - [c7]Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, Thomas E. Moore, W. Philip Kegelmeyer:
Distributed Pasting of Small Votes. Multiple Classifier Systems 2002: 52-61 - 2001
- [c6]Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, W. Philip Kegelmeyer:
Bagging Is a Small-Data-Set Phenomenon. CVPR (2) 2001: II:684-689 - [c5]Nitesh V. Chawla, Steven Eschrich, Lawrence O. Hall:
Creating Ensembles of Classifiers. ICDM 2001: 580-581 - [c4]Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, W. Philip Kegelmeyer:
Investigation of bagging-like effects and decision trees versus neural nets in protein secondary structure prediction. BIOKDD 2001: 50-59 - 2000
- [c3]Kevin W. Bowyer, Lawrence O. Hall, Thomas Moore, Nitesh V. Chawla, W. Philip Kegelmeyer:
A parallel decision tree builder for mining very large visualization datasets. SMC 2000: 1888-1893
1990 – 1999
- 1999
- [c2]Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowyer, W. Philip Kegelmeyer:
Learning Rules from Distributed Data. Large-Scale Parallel Data Mining 1999: 211-220 - 1998
- [c1]Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowyer:
Decision tree learning on very large data sets. SMC 1998: 2579-2584
Coauthor Index
Beenish M. Chaudhry
aka: Beenish Moalla Chaudhry
aka: Beenish Moalla Chaudhry
Damien Dablain
aka: Damien A. Dablain
aka: Damien A. Dablain
Sidney K. D'Mello
aka: Sidney D'Mello
aka: Sidney D'Mello
T. Ryan Hoens
aka: Thomas Ryan Hoens
aka: Thomas Ryan Hoens
Ryan Lichtenwalter
aka: Ryan N. Lichtenwalter
aka: Ryan N. Lichtenwalter
Aaron Striegel
aka: Aaron D. Striegel
aka: Aaron D. Striegel
[c244] [i117] [c223] [c222] [c219] [c215] [i107] [i102] [i97] [i83] [i82] [i81] [i80] [c210] [c209] [c208] [c205] [c203] [c201] [i79] [j87] [c196] [c194] [c193] [c192] [c191] [i68] [i66] [i64] [i63] [i59] [i58] [i56] [j77] [j72] [c186] [c178] [i51] [c177] [c176] [c171] [c168] [c163] [c159] [i47] [c157] [c144] [c141] [c136] [c135] [i37] [i36] [c129] [c123] [i33] [i31] [c117]
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