Nitesh V. Chawla

According to our database, Nitesh V. Chawla authored at least 367 papers between 1998 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Diet-ODIN: A Novel Framework for Opioid Misuse Detection with Interpretable Dietary Patterns.

Zheyuan Zhang, Zehong Wang, Shifu Hou, Evan Hall, Landon Bachman, Vincent Galassi, Jasmine White, Nitesh V. Chawla, Chuxu Zhang, Yanfang Ye

CoRR, 2024

Conformalized Selective Regression.

Anna Sokol, Nuno Moniz, Nitesh V. Chawla

CoRR, 2024

MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding.

Lirong Wu, Yijun Tian, Yufei Huang, Siyuan Li, Haitao Lin, Nitesh V. Chawla, Stan Z. Li

CoRR, 2024

Can we Soft Prompt LLMs for Graph Learning Tasks?

Zheyuan Liu, Xiaoxin He, Yijun Tian, Nitesh V. Chawla

CoRR, 2024

Node Duplication Improves Cold-start Link Prediction.

Zhichun Guo, Tong Zhao, Yozen Liu, Kaiwen Dong, William Shiao, Neil Shah, Nitesh V. Chawla

CoRR, 2024

UGMAE: A Unified Framework for Graph Masked Autoencoders.

Yijun Tian, Chuxu Zhang, Ziyi Kou, Zheyuan Liu, Xiangliang Zhang, Nitesh V. Chawla

CoRR, 2024

Universal Link Predictor By In-Context Learning on Graphs.

Kaiwen Dong, Haitao Mao, Zhichun Guo, Nitesh V. Chawla

CoRR, 2024

G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering.

Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh V. Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, Bryan Hooi

CoRR, 2024

Are we making much progress? Revisiting chemical reaction yield prediction from an imbalanced regression perspective.

Yihong Ma, Xiaobao Huang, Bozhao Nan, Nuno Moniz, Xiangliang Zhang, Olaf Wiest, Nitesh V. Chawla

CoRR, 2024

TinyLLM: Learning a Small Student from Multiple Large Language Models.

Yijun Tian, Yikun Han, Xiusi Chen, Wei Wang, Nitesh V. Chawla

CoRR, 2024

Large Language Model based Multi-Agents: A Survey of Progress and Challenges.

Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang

CoRR, 2024

Introduction to the Special Track on Artificial Intelligence and COVID-19 (Abstract Reprint).

Martin Michalowski, Robert Moskovitch, Nitesh V. Chawla

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Graph Neural Prompting with Large Language Models.

Yijun Tian, Huan Song, Zichen Wang, Haozhu Wang, Ziqing Hu, Fang Wang, Nitesh V. Chawla, Panpan Xu

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Deep Multimodal Complementarity Learning.

Daheng Wang, Tong Zhao, Wenhao Yu, Nitesh V. Chawla, Meng Jiang

IEEE Trans. Neural Networks Learn. Syst., December, 2023

DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data.

Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla

IEEE Trans. Neural Networks Learn. Syst., September, 2023

SD²: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance.

Zhichun Guo, Jun Tao, Siming Chen, Nitesh V. Chawla, Chaoli Wang

IEEE Trans. Vis. Comput. Graph., August, 2023

Information fusion via symbolic regression: A tutorial in the context of human health.

Jennifer J. Schnur, Nitesh V. Chawla

Inf. Fusion, April, 2023

Modeling Co-Evolution of Attributed and Structural Information in Graph Sequence.

Daheng Wang, Zhihan Zhang, Yihong Ma, Tong Zhao, Tianwen Jiang, Nitesh V. Chawla, Meng Jiang

IEEE Trans. Knowl. Data Eng., 2023

Introduction to the Special Track on Artificial Intelligence and COVID-19.

Martin Michalowski, Robert Moskovitch, Nitesh V. Chawla

J. Artif. Intell. Res., 2023

Detecting Anomalies in Small Unmanned Aerial Systems via Graphical Normalizing Flows.

Yihong Ma, Md Nafee Al Islam, Jane Cleland-Huang, Nitesh V. Chawla

IEEE Intell. Syst., 2023

Representing Outcome-driven Higher-order Dependencies in Graphs of Disease Trajectories.

Steven J. Krieg, Nitesh V. Chawla, Keith Feldman

CoRR, 2023

HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks.

Yihong Ma, Ning Yan, Jiayu Li, Masood S. Mortazavi, Nitesh V. Chawla

CoRR, 2023

Modeling non-uniform uncertainty in Reaction Prediction via Boosting and Dropout.

Taicheng Guo, Changsheng Ma, Xiuying Chen, Bozhao Nan, Kehan Guo, Shichao Pei, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang

CoRR, 2023

Pure Message Passing Can Estimate Common Neighbor for Link Prediction.

Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla

CoRR, 2023

What indeed can GPT models do in chemistry? A comprehensive benchmark on eight tasks.

Taicheng Guo, Kehan Guo, Bozhao Nan, Zhengwen Liang, Zhichun Guo, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang

CoRR, 2023

Towards Understanding How Data Augmentation Works with Imbalanced Data.

Damien A. Dablain, Nitesh V. Chawla

CoRR, 2023

Class-Imbalanced Learning on Graphs: A Survey.

Yihong Ma, Yijun Tian, Nuno Moniz, Nitesh V. Chawla

CoRR, 2023

Knowledge Distillation on Graphs: A Survey.

Yijun Tian, Shichao Pei, Xiangliang Zhang, Chuxu Zhang, Nitesh V. Chawla

CoRR, 2023

What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks.

Taicheng Guo, Kehan Guo, Bozhao Nan, Zhenwen Liang, Zhichun Guo, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Foundations and Applications in Large-scale AI Models: Pre-training, Fine-tuning, and Prompt-based Learning.

Derek Zhiyuan Cheng, Dhaval Patel, Linsey Pang, Sameep Mehta, Kexin Xie, Ed H. Chi, Wei Liu, Nitesh V. Chawla, James Bailey

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

KDD Workshop on Machine Learning in Finance.

Leman Akoglu, Nitesh V. Chawla, Senthil Kumar, Saurabh Nagrecha, Mahashweta Das, Vidyut M. Naware, Tanveer A. Faruquie

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Graph-based Molecular Representation Learning.

Zhichun Guo, Kehan Guo, Bozhao Nan, Yijun Tian, Roshni G. Iyer, Yihong Ma, Olaf Wiest, Xiangliang Zhang, Wei Wang, Chuxu Zhang, Nitesh V. Chawla

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

Linkless Link Prediction via Relational Distillation.

Zhichun Guo, William Shiao, Shichang Zhang, Yozen Liu, Nitesh V. Chawla, Neil Shah, Tong Zhao

Proceedings of the International Conference on Machine Learning, 2023

Chasing All-Round Graph Representation Robustness: Model, Training, and Optimization.

Chunhui Zhang, Yijun Tian, Mingxuan Ju, Zheyuan Liu, Yanfang Ye, Nitesh V. Chawla, Chuxu Zhang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Deep Ensembles for Graphs with Higher-order Dependencies.

Steven J. Krieg, William C. Burgis, Patrick M. Soga, Nitesh V. Chawla

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency.

Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, Nitesh V. Chawla

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Efficient Augmentation for Imbalanced Deep Learning.

Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk, Nitesh V. Chawla

Proceedings of the 39th IEEE International Conference on Data Engineering, 2023

Fairness-Aware Mixture of Experts with Interpretability Budgets.

Joe Germino, Nuno Moniz, Nitesh V. Chawla

Proceedings of the Discovery Science - 26th International Conference, 2023

Cross-Domain Few-Shot Graph Classification with a Reinforced Task Coordinator.

Qiannan Zhang, Shichao Pei, Qiang Yang, Chuxu Zhang, Nitesh V. Chawla, Xiangliang Zhang

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

Boosting Graph Neural Networks via Adaptive Knowledge Distillation.

Zhichun Guo, Chunhui Zhang, Yujie Fan, Yijun Tian, Chuxu Zhang, Nitesh V. Chawla

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

Heterogeneous Graph Masked Autoencoders.

Yijun Tian, Kaiwen Dong, Chunhui Zhang, Chuxu Zhang, Nitesh V. Chawla

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Representation Learning on Variable Length and Incomplete Wearable-Sensory Time Series.

Xian Wu, Chao Huang, Pablo Robles-Granda, Nitesh V. Chawla

ACM Trans. Intell. Syst. Technol., 2022

Formative Evaluation of a Tablet Application to Support Goal-Oriented Care in Community-Dwelling Older Adults.

Beenish Moalla Chaudhry, Dipanwita Dasgupta, Nitesh V. Chawla

Proc. ACM Hum. Comput. Interact., 2022

Data-driven testing program improves detection of COVID-19 cases and reduces community transmission.

Steven J. Krieg, Carolina Avendano, Evan Grantham-Brown, Aaron Lilienfeld Asbun, Jennifer J. Schnur, Marie Lynn Miranda, Nitesh V. Chawla

npj Digit. Medicine, 2022

FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings.

Piotr Bielak, Kamil Tagowski, Maciej Falkiewicz, Tomasz Kajdanowicz, Nitesh V. Chawla

Knowl. Based Syst., 2022

Graph Barlow Twins: A self-supervised representation learning framework for graphs.

Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla

Knowl. Based Syst., 2022

AttrE2vec: Unsupervised attributed edge representation learning.

Piotr Bielak, Tomasz Kajdanowicz, Nitesh V. Chawla

Inf. Sci., 2022

Heterogeneous relational reasoning in knowledge graphs with reinforcement learning.

Mandana Saebi, Steven Kreig, Chuxu Zhang, Meng Jiang, Tomasz Kajdanowicz, Nitesh V. Chawla

Inf. Fusion, 2022

A Survey on Healthy Food Decision Influences Through Technological Innovations.

Jermaine Marshall, Priscilla Jiménez-Pazmino, Ronald A. Metoyer, Nitesh V. Chawla

ACM Trans. Comput. Heal., 2022

Flexibility Versus Routineness in Multimodal Health Indicators: A Sensor-based Longitudinal in Situ Study of Information Workers.

Mary Jean Amon, Stephen M. Mattingly, Aaron Necaise, Gloria Mark, Nitesh V. Chawla, Anind K. Dey, Sidney D'Mello

ACM Trans. Comput. Heal., 2022

Interpretable ML for Imbalanced Data.

Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk, David W. Aha, Nitesh V. Chawla

CoRR, 2022

Privacy-Preserving Data Synthetisation for Secure Information Sharing.

Tânia Carvalho, Nuno Moniz, Pedro Faria, Luís Antunes, Nitesh V. Chawla

CoRR, 2022

Understanding CNN Fragility When Learning With Imbalanced Data.

Damien Dablain, Kristen N. Jacobson, Colin Bellinger, Mark Roberts, Nitesh V. Chawla

CoRR, 2022

NOSMOG: Learning Noise-robust and Structure-aware MLPs on Graphs.

Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, Nitesh V. Chawla

CoRR, 2022

Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning.

Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla

CoRR, 2022

Graph-based Molecular Representation Learning.

Zhichun Guo, Bozhao Nan, Yijun Tian, Olaf Wiest, Chuxu Zhang, Nitesh V. Chawla

CoRR, 2022

SD2: Slicing and Dicing Scholarly Data for Interactive Evaluation of Academic Performance.

Zhichun Guo, Jun Tao, Siming Chen, Nitesh V. Chawla, Chaoli Wang

CoRR, 2022

Few-Shot Learning on Graphs: A Survey.

Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu

CoRR, 2022

Predicting Terrorist Attacks in the United States using Localized News Data.

Steven J. Krieg, Christian W. Smith, Rusha Chatterjee, Nitesh V. Chawla

CoRR, 2022

Graph Minimally-supervised Learning.

Kaize Ding, Jundong Li, Nitesh V. Chawla, Huan Liu

Proceedings of the WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21, 2022

RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers.

Md Nafee Al Islam, Yihong Ma, Pedro Alarcon Granadeno, Nitesh V. Chawla, Jane Cleland-Huang

Proceedings of the 30th IEEE International Requirements Engineering Conference, 2022

FakeEdge: Alleviate Dataset Shift in Link Prediction.

Kaiwen Dong, Yijun Tian, Zhichun Guo, Yang Yang, Nitesh V. Chawla

Proceedings of the Learning on Graphs Conference, 2022

KDD Workshop on Machine Learning in Finance.

Senthil Kumar, Leman Akoglu, Nitesh V. Chawla, Saurabh Nagrecha, Vidyut M. Naware, Tanveer A. Faruquie, Hays McCormick

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Toward Graph Minimally-Supervised Learning.

Kaize Ding, Chuxu Zhang, Jie Tang, Nitesh V. Chawla, Huan Liu

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Few-Shot Learning on Graphs.

Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks.

Yijun Tian, Chuxu Zhang, Zhichun Guo, Yihong Ma, Ronald A. Metoyer, Nitesh V. Chawla

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation.

Yijun Tian, Chuxu Zhang, Zhichun Guo, Chao Huang, Ronald A. Metoyer, Nitesh V. Chawla

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Compositional Training for End-to-End Deep AUC Maximization.

Zhuoning Yuan, Zhishuai Guo, Nitesh V. Chawla, Tianbao Yang

Proceedings of the Tenth International Conference on Learning Representations, 2022

Malicious Repositories Detection with Adversarial Heterogeneous Graph Contrastive Learning.

Yiyue Qian, Yiming Zhang, Nitesh V. Chawla, Yanfang Ye, Chuxu Zhang

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

Hierarchical Spatio-Temporal Graph Neural Networks for Pandemic Forecasting.

Yihong Ma, Patrick Gérard, Yijun Tian, Zhichun Guo, Nitesh V. Chawla

Proceedings of the 31st ACM International Conference on Information & Knowledge Management, 2022

2021

Inductive Contextual Relation Learning for Personalization.

Chuxu Zhang, Huaxiu Yao, Lu Yu, Chao Huang, Dongjin Song, Haifeng Chen, Meng Jiang, Nitesh V. Chawla

ACM Trans. Inf. Syst., 2021

Modeling Complementarity in Behavior Data with Multi-Type Itemset Embedding.

Daheng Wang, Qingkai Zeng, Nitesh V. Chawla, Meng Jiang

ACM Trans. Intell. Syst. Technol., 2021

Biomedical Knowledge Graphs Construction From Conditional Statements.

Tianwen Jiang, Qingkai Zeng, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang

IEEE ACM Trans. Comput. Biol. Bioinform., 2021

Predicting Job Performance Using Mobile Sensing.

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

IEEE Pervasive Comput., 2021

Examining Response to Negative Life Events Through Fitness Tracker Data.

Louis Faust, Keith Feldman, Suwen Lin, Stephen M. Mattingly, Sidney D'Mello, Nitesh V. Chawla

Frontiers Digit. Health, 2021

Recipe Recommendation With Hierarchical Graph Attention Network.

Yijun Tian, Chuxu Zhang, Ronald A. Metoyer, Nitesh V. Chawla

Frontiers Big Data, 2021

Unified Representation of Twitter and Online News Using Graph and Entities.

Munira Syed, Daheng Wang, Meng Jiang, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla

Frontiers Big Data, 2021

Pandemic Pulse: Unraveling and Modeling Social Signals During the COVID-19 Pandemic.

Steven J. Krieg, Jennifer J. Schnur, Jermaine D. Marshall, Matthew M. Schoenbauer, Nitesh V. Chawla

Digit. Gov. Res. Pract., 2021

Jointly Predicting Job Performance, Personality, Cognitive Ability, Affect, and Well-Being.

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

IEEE Comput. Intell. Mag., 2021

Few-Shot Graph Learning for Molecular Property Prediction.

Zhichun Guo, Chuxu Zhang, Wenhao Yu, John Herr, Olaf Wiest, Meng Jiang, Nitesh V. Chawla

Proceedings of the WWW '21: The Web Conference 2021, 2021

An Optimized NL2SQL System for Enterprise Data Mart.

Kaiwen Dong, Kai Lu, Xin Xia, David A. Cieslak, Nitesh V. Chawla

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track, 2021

Lan: Learning to Augment Noise Tolerance for Self-report Survey Labels.

Suwen Lin, Louis Faust, Nitesh V. Chawla

Proceedings of the 19th IEEE International Conference on Pervasive Computing and Communications, 2021

Machine Learning in Finance.

Senthil Kumar, Leman Akoglu, Nitesh V. Chawla, José A. Rodríguez-Serrano, Tanveer A. Faruquie, Saurabh Nagrecha

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Dynamic Attributed Graph Prediction with Conditional Normalizing Flows.

Daheng Wang, Tong Zhao, Nitesh V. Chawla, Meng Jiang

Proceedings of the IEEE International Conference on Data Mining, 2021

A Qualitative Usability Evaluation of Tablets and Accessibility Settings by Older Adults.

Dipanwita Dasgupta, Beenish M. Chaudhry, Nitesh V. Chawla

Proceedings of the HCI International 2021 - Late Breaking Papers: Cognition, Inclusion, Learning, and Culture, 2021

Teaching Tablet Technology to Older Adults.

Beenish M. Chaudhry, Dipanwita Dasgupta, Mona A. Mohamed, Nitesh V. Chawla

Proceedings of the HCI International 2021 - Late Breaking Papers: Cognition, Inclusion, Learning, and Culture, 2021

motif2vec: Semantic-aware Representation Learning for Wearables' Time Series Data.

Suwen Lin, Xian Wu, Nitesh V. Chawla

Proceedings of the 8th IEEE International Conference on Data Science and Advanced Analytics, 2021

Recipe Representation Learning with Networks.

Yijun Tian, Chuxu Zhang, Ronald A. Metoyer, Nitesh V. Chawla

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

2020

Scalable Uncertainty-Aware Truth Discovery in Big Data Social Sensing Applications for Cyber-Physical Systems.

Chao Huang, Dong Wang, Nitesh V. Chawla

IEEE Trans. Big Data, 2020

Deviations from normal bedtimes are associated with short-term increases in resting heart rate.

Louis Faust, Keith Feldman, Stephen M. Mattingly, David Hachen, Nitesh V. Chawla

npj Digit. Medicine, 2020

Efficient modeling of higher-order dependencies in networks: from algorithm to application for anomaly detection.

Mandana Saebi, Jian Xu, Lance M. Kaplan, Bruno Ribeiro, Nitesh V. Chawla

EPJ Data Sci., 2020

Learning Attribute-Structure Co-Evolutions in Dynamic Graphs.

Daheng Wang, Zhihan Zhang, Yihong Ma, Tong Zhao, Tianwen Jiang, Nitesh V. Chawla, Meng Jiang

CoRR, 2020

Canonicalizing Open Knowledge Bases with Multi-Layered Meta-Graph Neural Network.

Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang

CoRR, 2020

Heterogeneous Relational Reasoning in Knowledge Graphs with Reinforcement Learning.

Mandana Saebi, Steven J. Krieg, Chuxu Zhang, Meng Jiang, Nitesh V. Chawla

CoRR, 2020

HONEM: Learning Embedding for Higher Order Networks.

Mandana Saebi, Giovanni Luca Ciampaglia, Lance M. Kaplan, Nitesh V. Chawla

Big Data, 2020

Fuzzy Relative Willingness: Modeling Influence of Exogenous Factors in Driving Information Propagation Through a Social Network.

Suman Kundu, Tomasz Kajdanowicz, Przemyslaw Kazienko, Nitesh V. Chawla

IEEE Access, 2020

Hierarchically Structured Transformer Networks for Fine-Grained Spatial Event Forecasting.

Xian Wu, Chao Huang, Chuxu Zhang, Nitesh V. Chawla

Proceedings of the WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, 2020

Learning from Cross-Modal Behavior Dynamics with Graph-Regularized Neural Contextual Bandit.

Xian Wu, Suleyman Cetintas, Deguang Kong, Miao Lu, Jian Yang, Nitesh V. Chawla

Proceedings of the WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, 2020

Characterizing Exploratory Behaviors on a Personal Visualization Interface Using Interaction Logs.

Poorna Talkad Sukumar, Gonzalo J. Martínez, Ted Grover, Gloria Mark, Sidney K. D'Mello, Nitesh V. Chawla, Stephen M. Mattingly, Aaron D. Striegel

Proceedings of the 22nd Eurographics Conference on Visualization, 2020

Filling Missing Values on Wearable-Sensory Time Series Data.

Suwen Lin, Xian Wu, Gonzalo J. Martínez, Nitesh V. Chawla

Proceedings of the 2020 SIAM International Conference on Data Mining, 2020

Multi-modal Network Representation Learning.

Chuxu Zhang, Meng Jiang, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Calendar Graph Neural Networks for Modeling Time Structures in Spatiotemporal User Behaviors.

Daheng Wang, Meng Jiang, Munira Syed, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Fighting a Pandemic: Convergence of Expertise, Data Science and Policy.

Tina Eliassi-Rad, Nitesh V. Chawla, Vittoria Colizza, Lauren Gardner, Marcel Salathé, Samuel V. Scarpino, Joseph T. Wu

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

Higher-order Networks of Diabetes Comorbidities: Disease Trajectories that Matter.

Steven J. Krieg, Daniel H. Robertson, Meeta P. Pradhan, Nitesh V. Chawla

Proceedings of the 8th IEEE International Conference on Healthcare Informatics, 2020

Identifying Bridge Users: the Knowledge Transfer Agents in Enterprise Collaboration Systems.

Priscilla Jiménez-Pazmino, Trenton Ford, Ronald A. Metoyer, Nitesh V. Chawla

Proceedings of the 53rd Hawaii International Conference on System Sciences, 2020

Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases.

Chuxu Zhang, Lu Yu, Mandana Saebi, Meng Jiang, Nitesh V. Chawla

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020

GrowHON: A Scalable Algorithm for Growing Higher-order Networks of Sequences.

Steven J. Krieg, Peter M. Kogge, Nitesh V. Chawla

Proceedings of the Complex Networks & Their Applications IX, 2020

Personalized Imputation on Wearable-Sensory Time Series via Knowledge Transfer.

Xian Wu, Stephen M. Mattingly, Shayan Mirjafari, Chao Huang, Nitesh V. Chawla

Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

GraSeq: Graph and Sequence Fusion Learning for Molecular Property Prediction.

Zhichun Guo, Wenhao Yu, Chuxu Zhang, Meng Jiang, Nitesh V. Chawla

Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

MBead: Semi-supervised Multilabel Behaviour Anomaly Detection on Multivariate Temporal Sensory Data.

Suwen Lin, Louis Faust, Sidney D'Mello, Gonzalo J. Martínez, Nitesh V. Chawla

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

Imputing Growth Snapshot Similarity in Early Childhood Development: A Tensor Decomposition Approach.

Jennifer J. Schnur, Ryan Karl, Angélica García-Martínez, Meng Jiang, Nitesh V. Chawla

Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, 2020

Overcoming Data Sparsity in Predicting User Characteristics from Behavior through Graph Embeddings.

Munira Syed, Daheng Wang, Meng Jiang, Oliver Conway, Vishal Juneja, Sriram Subramanian, Nitesh V. Chawla

Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2020

Few-Shot Knowledge Graph Completion.

Chuxu Zhang, Huaxiu Yao, Chao Huang, Meng Jiang, Zhenhui Li, Nitesh V. Chawla

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Graph Few-Shot Learning via Knowledge Transfer.

Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh V. Chawla, Zhenhui Li

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Multi-Label Patent Categorization with Non-Local Attention-Based Graph Convolutional Network.

Pingjie Tang, Meng Jiang, Bryan (Ning) Xia, Jed W. Pitera, Jeffrey Welser, Nitesh V. Chawla

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps.

Jun Tao, Martin Imre, Chaoli Wang, Nitesh V. Chawla, Hanqi Guo, Gokhan Sever, Seung Hyun Kim

IEEE Trans. Vis. Comput. Graph., 2019

Multi-Label Learning from Crowds.

Shao-Yuan Li, Yuan Jiang, Nitesh V. Chawla, Zhi-Hua Zhou

IEEE Trans. Knowl. Data Eng., 2019

Characterizing online health and wellness information consumption: A study.

Aastha Nigam, Reid A. Johnson, Dong Wang, Nitesh V. Chawla

Inf. Fusion, 2019

A Multisensor Person-Centered Approach to Understand the Role of Daily Activities in Job Performance with Organizational Personas.

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

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2019

Differentiating Higher and Lower Job Performers in the Workplace Using Mobile Sensing.

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

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2019

Learning in the presence of class imbalance and concept drift.

Shuo Wang, Leandro L. Minku, Nitesh V. Chawla, Xin Yao

Neurocomputing, 2019

HONEM: Network Embedding Using Higher-Order Patterns in Sequential Data.

Mandana Saebi, Giovanni Luca Ciampaglia, Lance M. Kaplan, Nitesh V. Chawla

CoRR, 2019

Constructing Information-Lossless Biological Knowledge Graphs from Conditional Statements.

Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang

CoRR, 2019

Incremental embedding for temporal networks.

Tomasz Kajdanowicz, Kamil Tagowski, Maciej Falkiewicz, Piotr Bielak, Przemyslaw Kazienko, Nitesh V. Chawla

CoRR, 2019

Learning from data streams and class imbalance.

Shuo Wang, Leandro L. Minku, Nitesh V. Chawla, Xin Yao

Connect. Sci., 2019

MiST: A Multiview and Multimodal Spatial-Temporal Learning Framework for Citywide Abnormal Event Forecasting.

Chao Huang, Chuxu Zhang, Jiashu Zhao, Xian Wu, Nitesh V. Chawla, Dawei Yin

Proceedings of the World Wide Web Conference, 2019

SHNE: Representation Learning for Semantic-Associated Heterogeneous Networks.

Chuxu Zhang, Ananthram Swami, Nitesh V. Chawla

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019

Neural Tensor Factorization for Temporal Interaction Learning.

Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Nitesh V. Chawla

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019

What 30 Days Tells Us About 3 Years: Identifying Early Signs of User Abandonment and Non-Adherence.

Louis Faust, Priscilla Jiménez-Pazmino, James K. Holland, Omar Lizardo, David Hachen, Nitesh V. Chawla

Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare, 2019

From Design to Development to Evaluation of a Pregnancy App for Low-Income Women in a Community-Based Setting.

Beenish M. Chaudhry, Louis Faust, Nitesh V. Chawla

Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services, 2019

Integrated Closed-loop Learning Analytics Scheme in a First Year Experience Course.

Munira Syed, Trunojoyo Anggara, Alison Lanski, Xiaojing Duan, G. Alex Ambrose, Nitesh V. Chawla

Proceedings of the 9th International Conference on Learning Analytics & Knowledge, 2019

Heterogeneous Graph Neural Network.

Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, Nitesh V. Chawla

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

TUBE: Embedding Behavior Outcomes for Predicting Success.

Daheng Wang, Tianwen Jiang, Nitesh V. Chawla, Meng Jiang

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

The Role of: A Novel Scientific Knowledge Graph Representation and Construction Model.

Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Online Purchase Prediction via Multi-Scale Modeling of Behavior Dynamics.

Chao Huang, Xian Wu, Xuchao Zhang, Chuxu Zhang, Jiashu Zhao, Dawei Yin, Nitesh V. Chawla

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Statistical Analysis and Modeling of Heterogeneous Workloads on Amazon's Public Cloud Infrastructure.

Frederick Nwanganga, Nitesh V. Chawla, Gregory R. Madey

Proceedings of the 52nd Hawaii International Conference on System Sciences, 2019

Multi-Input Multi-Output Sequence Labeling for Joint Extraction of Fact and Condition Tuples from Scientific Text.

Tianwen Jiang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

Implicit and Explicit Emotions in MOOCs.

Munira Syed, Malolan Chetlur, Shazia Afzal, G. Alex Ambrose, Nitesh V. Chawla

Proceedings of the 12th International Conference on Educational Data Mining, 2019

Development and Evaluation of a Web Application for Prenatal Care Coordinators in the United States.

Beenish Moalla Chaudhry, Louis Faust, Nitesh V. Chawla

Proceedings of the Extending the Boundaries of Design Science Theory and Practice, 2019

Gender Prediction Through Synthetic Resampling of User Profiles Using SeqGANs.

Munira Syed, Jermaine Marshall, Aastha Nigam, Nitesh V. Chawla

Proceedings of the Computational Data and Social Networks - 8th International Conference, 2019

Deep Prototypical Networks for Imbalanced Time Series Classification under Data Scarcity.

Chao Huang, Xian Wu, Xuchao Zhang, Suwen Lin, Nitesh V. Chawla

Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019

Similarity-Aware Network Embedding with Self-Paced Learning.

Chao Huang, Baoxu Shi, Xuchao Zhang, Xian Wu, Nitesh V. Chawla

Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019

Social Media as a Passive Sensor in Longitudinal Studies of Human Behavior and Wellbeing.

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

Proceedings of the Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, 2019

The Tesserae Project: Large-Scale, Longitudinal, <i>In Situ, </i> Multimodal Sensing of Information Workers.

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

Proceedings of the Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems, 2019

CTGA: Graph-based Biomedical Literature Search.

Tianwen Jiang, Zhihan Zhang, Tong Zhao, Bing Qin, Ting Liu, Nitesh V. Chawla, Meng Jiang

Proceedings of the 2019 IEEE International Conference on Bioinformatics and Biomedicine, 2019

Outside the Hospital Walls: Associations of Value Based Care Metrics and Community Health Factors.

Catherine Markley, Keith Feldman, Nitesh V. Chawla

Proceedings of the 2019 IEEE EMBS International Conference on Biomedical & Health Informatics, 2019

Imputing Missing Social Media Data Stream in Multisensor Studies of Human Behavior.

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

Proceedings of the 8th International Conference on Affective Computing and Intelligent Interaction, 2019

A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data.

Chuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang Feng, Cristian Lumezanu, Wei Cheng, Jingchao Ni, Bo Zong, Haifeng Chen, Nitesh V. Chawla

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

Using Structural Similarity to Predict Future Workload Behavior in the Cloud.

Frederick Nwanganga, Nitesh V. Chawla

Proceedings of the 12th IEEE International Conference on Cloud Computing, 2019

2018

Link Prediction: A Primer.

Yang Yang, Nitesh V. Chawla

Proceedings of the Encyclopedia of Social Network Analysis and Mining, 2nd Edition, 2018

Semantic Flow Graph: A Framework for Discovering Object Relationships in Flow Fields.

Jun Tao, Chaoli Wang, Nitesh V. Chawla, Lei Shi, Seung Hyun Kim

IEEE Trans. Vis. Comput. Graph., 2018

Will Triadic Closure Strengthen Ties in Social Networks?

Hong Huang, Yuxiao Dong, Jie Tang, Hongxia Yang, Nitesh V. Chawla, Xiaoming Fu

ACM Trans. Knowl. Discov. Data, 2018

TIQS: Targeted Iterative Question Selection for Health Interventions.

Keith Feldman, Spyros Kotoulas, Nitesh V. Chawla

J. Heal. Informatics Res., 2018

The State of Data in Healthcare: Path Towards Standardization.

Keith Feldman, Reid A. Johnson, Nitesh V. Chawla

J. Heal. Informatics Res., 2018

SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary.

Alberto Fernández, Salvador García, Francisco Herrera, Nitesh V. Chawla

J. Artif. Intell. Res., 2018

Big data fusion in Internet of Things.

Zheng Yan, Jun Liu, Laurence T. Yang, Nitesh V. Chawla

Inf. Fusion, 2018

LNEMLC: Label Network Embeddings for Multi-Label Classifiation.

Piotr Szymanski, Tomasz Kajdanowicz, Nitesh V. Chawla

CoRR, 2018

Social Network Structure is Predictive of Health and Wellness.

Suwen Lin, Louis Faust, Pablo Robles-Granda, Nitesh V. Chawla

CoRR, 2018

CARL: Content-Aware Representation Learning for Heterogeneous Networks.

Chuxu Zhang, Ananthram Swami, Nitesh V. Chawla

CoRR, 2018

Long-term Compliance Habits: What Early Data Tells Us.

Louis Faust, Priscilla Jiménez, David Hachen, Omar Lizardo, Aaron Striegel, Nitesh V. Chawla

CoRR, 2018

Neural Tensor Factorization.

Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Nitesh V. Chawla

CoRR, 2018

FraudBuster: Reducing Fraud in an Auto Insurance Market.

Saurabh Nagrecha, Reid A. Johnson, Nitesh V. Chawla

Big Data, 2018

Predicting onset of complications from diabetes: a graph based approach.

Pamela Bilo Thomas, Daniel H. Robertson, Nitesh V. Chawla

Appl. Netw. Sci., 2018

Camel: Content-Aware and Meta-path Augmented Metric Learning for Author Identification.

Chuxu Zhang, Chao Huang, Lu Yu, Xiangliang Zhang, Nitesh V. Chawla

Proceedings of the 2018 World Wide Web Conference on World Wide Web, 2018

Who will Attend This Event Together? Event Attendance Prediction via Deep LSTM Networks.

Xian Wu, Yuxiao Dong, Baoxu Shi, Ananthram Swami, Nitesh V. Chawla

Proceedings of the 2018 SIAM International Conference on Data Mining, 2018

ONE-M: Modeling the Co-evolution of Opinions and Network Connections.

Aastha Nigam, Kijung Shin, Ashwin Bahulkar, Bryan Hooi, David Hachen, Boleslaw K. Szymanski, Christos Faloutsos, Nitesh V. Chawla

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

Towards an Integrated mHealth Platform for Community-based Maternity Health Workers in Low-Income Communities.

Beenish Moalla Chaudhry, Louis Faust, Nitesh V. Chawla

Proceedings of the 12th EAI International Conference on Pervasive Computing Technologies for Healthcare, 2018

Multi-Type Itemset Embedding for Learning Behavior Success.

Daheng Wang, Meng Jiang, Qingkai Zeng, Zachary Eberhart, Nitesh V. Chawla

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

VisPod: Content-Based Audio Visual Navigation.

Qiyu Zhi, Suwen Lin, Shuai He, Ronald A. Metoyer, Nitesh V. Chawla

Proceedings of the 23rd International Conference on Intelligent User Interfaces Companion, 2018

Task-Guided and Semantic-Aware Ranking for Academic Author-Paper Correlation Inference.

Chuxu Zhang, Lu Yu, Xiangliang Zhang, Nitesh V. Chawla

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Quantifying Subjective Well-Being Using Trends in Weekend Activity.

Louis Faust, David Hachen, Omar Lizardo, Nitesh V. Chawla

Proceedings of the IEEE International Conference on Healthcare Informatics, 2018

SMOTEBoost for Regression: Improving the Prediction of Extreme Values.

Nuno Moniz, Rita P. Ribeiro, Vítor Cerqueira, Nitesh V. Chawla

Proceedings of the 5th IEEE International Conference on Data Science and Advanced Analytics, 2018

RESTFul: Resolution-Aware Forecasting of Behavioral Time Series Data.

Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Louis Faust, Nitesh V. Chawla

Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018

DeepCrime: Attentive Hierarchical Recurrent Networks for Crime Prediction.

Chao Huang, Junbo Zhang, Yu Zheng, Nitesh V. Chawla

Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018

Leveraging health and wellness platforms to understand childhood obesity: A usability pilot of FitSpace.

Keith Feldman, Mayra Duarte, Waldo Mikels-Carrasco, Nitesh V. Chawla

Proceedings of the 2018 IEEE EMBS International Conference on Biomedical & Health Informatics, 2018

2017

User Modeling on Demographic Attributes in Big Mobile Social Networks.

Yuxiao Dong, Nitesh V. Chawla, Jie Tang, Yang Yang, Yang Yang

ACM Trans. Inf. Syst., 2017

Why is quantification an interesting learning problem?

Pablo González, Jorge Díez, Nitesh V. Chawla, Juan José del Coz

Prog. Artif. Intell., 2017

Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier.

Md Mursalin, Yuan Zhang, Yuehui Chen, Nitesh V. Chawla

Neurocomputing, 2017

A Review on Quantification Learning.

Pablo González, Alberto Castaño, Nitesh V. Chawla, Juan José del Coz

ACM Comput. Surv., 2017

Detecting Anomalies in Sequential Data with Higher-order Networks.

Jian Xu, Mandana Saebi, Bruno Ribeiro, Lance M. Kaplan, Nitesh V. Chawla

CoRR, 2017

Proceedings of the IJCAI 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17).

Shuo Wang, Leandro L. Minku, Nitesh V. Chawla, Xin Yao

CoRR, 2017

Influence of Personal Preferences on Link Dynamics in Social Networks.

Ashwin Bahulkar, Boleslaw K. Szymanski, Nitesh V. Chawla, Omar Lizardo, Kevin S. Chan

Complex., 2017

Harvesting Social Signals to Inform Peace Processes Implementation and Monitoring.

Aastha Nigam, Henry K. Dambanemuya, Madhav Joshi, Nitesh V. Chawla

Big Data, 2017

MOOC Dropout Prediction: Lessons Learned from Making Pipelines Interpretable.

Saurabh Nagrecha, John Z. Dillon, Nitesh V. Chawla

Proceedings of the 26th International Conference on World Wide Web Companion, 2017

UAPD: Predicting Urban Anomalies from Spatial-Temporal Data.

Xian Wu, Yuxiao Dong, Chao Huang, Jian Xu, Dong Wang, Nitesh V. Chawla

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Poster: RSSI-Based Pedestrian Localization Using Artificial Neural Networks.

Mehdi Golestanian, Christian Poellabauer, Nitesh V. Chawla

Proceedings of the 2nd ACM International Workshop on Smart, 2017

Structural Diversity and Homophily: A Study Across More Than One Hundred Big Networks.

Yuxiao Dong, Reid A. Johnson, Jian Xu, Nitesh V. Chawla

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

metapath2vec: Scalable Representation Learning for Heterogeneous Networks.

Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

Visual Analytics of Higher-order Dependencies in Sensor Data: Demo Abstract.

Jian Xu, Jun Tao, Nitesh V. Chawla, Chaoli Wang

Proceedings of the Second International Conference on Internet-of-Things Design and Implementation, 2017

Materials Science Literature-Patent Relevance Search: A Heterogeneous Network Analysis Approach.

Pingjie Tang, Jed Pitera, Dmitry Zubarev, Nitesh V. Chawla

Proceedings of the 2017 IEEE International Conference on Data Science and Advanced Analytics, 2017

Exploring Compliance: Observations from a Large Scale Fitbit Study.

Louis Faust, Rachael Purta, David Hachen, Aaron Striegel, Christian Poellabauer, Omar Lizardo, Nitesh V. Chawla

Proceedings of the 2nd International Workshop on Social Sensing, 2017

Predicting Chronic Heart Failure Using Diagnoses Graphs.

Saurabh Nagrecha, Pamela Bilo Thomas, Keith Feldman, Nitesh V. Chawla

Proceedings of the Machine Learning and Knowledge Extraction, 2017

ImWalkMF: Joint matrix factorization and implicit walk integrative learning for recommendation.

Chuxu Zhang, Lu Yu, Xiangliang Zhang, Nitesh V. Chawla

Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017

Reliable fake review detection via modeling temporal and behavioral patterns.

Xian Wu, Yuxiao Dong, Jun Tao, Chao Huang, Nitesh V. Chawla

Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017

Mining Features Associated with Effective Tweets.

Jian Xu, Nitesh V. Chawla

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017, Sydney, Australia, July 31, 2017

HoNVis: Visualizing and exploring higher-order networks.

Jun Tao, Jian Xu, Chaoli Wang, Nitesh V. Chawla

Proceedings of the 2017 IEEE Pacific Visualization Symposium, 2017

The ABC of MOOCs: Affect and its inter-play with behavior and cognition.

Shazia Afzal, Bikram Sengupta, Munira Syed, Nitesh V. Chawla, G. Alex Ambrose, Malolan Chetlur

Proceedings of the Seventh International Conference on Affective Computing and Intelligent Interaction, 2017

A Minimum-Cost Flow Model for Workload Optimization on Cloud Infrastructure.

Frederick Nwanganga, Mandana Saebi, Gregory R. Madey, Nitesh V. Chawla

Proceedings of the 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), 2017

2016

Editorial IEEE Transactions on Neural Networks and Learning Systems 2016 and Beyond.

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

IEEE Trans. Neural Networks Learn. Syst., 2016

Can Scientific Impact Be Predicted?

Yuxiao Dong, Reid A. Johnson, Nitesh V. Chawla

IEEE Trans. Big Data, 2016

Current and Future Challenges in Mining Large Networks: Report on the Second SDM Workshop on Mining Networks and Graphs.

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

SIGKDD Explor., 2016

Prediction of fine-tuned promoter activity from DNA sequence.

Geoffrey Siwo, Andrew K. Rider, Asako Tan, Richard S. Pinapati, Scott J. Emrich, Nitesh V. Chawla, Michael T. Ferdig

F1000Research, 2016

Quantifying decision making for data science: from data acquisition to modeling.

Saurabh Nagrecha, Nitesh V. Chawla

EPJ Data Sci., 2016

Gender Differences in Communication Behaviors, Spatial Proximity Patterns, and Mobility Habits.

Yang Yang, Omar Lizardo, Dong Wang, Yuxiao Dong, Aaron D. Striegel, David Hachen, Nitesh V. Chawla

CoRR, 2016

Influence Activation Model: A New Perspective in Social Influence Analysis and Social Network Evolution.

Yang Yang, Nitesh V. Chawla, Ryan N. Lichtenwalter, Yuxiao Dong

CoRR, 2016

Do the Young Live in a "Smaller World" Than the Old? Age-Specific Degrees of Separation in a Large-Scale Mobile Communication Network.

Yuxiao Dong, Omar Lizardo, Nitesh V. Chawla

CoRR, 2016

Structural Diversity and Homophily: A Study Across More than One Hundred Large-Scale Networks.

Yuxiao Dong, Reid A. Johnson, Jian Xu, Nitesh V. Chawla

CoRR, 2016

Ieee Access Special Section Editorial: Big Data Analytics for Smart and Connected Health.

Yuan Zhang, Lin Zhang, Eiji Oki, Nitesh V. Chawla, Anton Kos

IEEE Access, 2016

A Survey of Tablet Applications for Promoting Successful Aging in Older Adults.

Dipanwita Dasgupta, Beenish M. Chaudhry, Emily Koh, Nitesh V. Chawla

IEEE Access, 2016

Successful aging for low-income older adults: towards design principles.

Beenish M. Chaudhry, Kimberly Green Reeves, Nitesh V. Chawla

Proceedings of the 10th EAI International Conference on Pervasive Computing Technologies for Healthcare, 2016

Developing health technologies for older adults: methodological and ethical considerations.

Beenish M. Chaudhry, Mayra Duarte, Nitesh V. Chawla, Dipanwita Dasgupta

Proceedings of the 10th EAI International Conference on Pervasive Computing Technologies for Healthcare, 2016

Deep learning for network analysis: Problems, approaches and challenges.

Siddharth Pal, Yuxiao Dong, Bishal Thapa, Nitesh V. Chawla, Ananthram Swami, Ram Ramanathan

Proceedings of the 2016 IEEE Military Communications Conference, 2016

Mining the Clinical Narrative: All Text are Not Equal.

Keith Feldman, Nicholas L. Hazekamp, Nitesh V. Chawla

Proceedings of the 2016 IEEE International Conference on Healthcare Informatics, 2016

eSeniorCare: Technology for Promoting Well-Being of Older Adults in Independent Living Facilities.

Dipanwita Dasgupta, Kimberly Green Reeves, Beenish M. Chaudhry, Mayra Duarte, Nitesh V. Chawla

Proceedings of the 2016 IEEE International Conference on Healthcare Informatics, 2016

MedCare: Leveraging Medication Similarity for Disease Prediction.

Dipanwita Dasgupta, Nitesh V. Chawla

Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics, 2016

Connecting the dots to infer followers' topical interest on Twitter.

Aastha Nigam, Salvador Aguiñaga, Nitesh V. Chawla

Proceedings of the 2016 International Conference on Behavioral, 2016

Analysis of link formation, persistence and dissolution in NetSense data.

Ashwin Bahulkar, Boleslaw K. Szymanski, Omar Lizardo, Yuxiao Dong, Yang Yang, Nitesh V. Chawla

Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2016

Design and Evaluation of a Medication Adherence Application with Communication for Seniors in Independent Living Communities.

Dipanwita Dasgupta, Reid A. Johnson, Beenish M. Chaudhry, Kimberly Green Reeves, Patty Willaert, Nitesh V. Chawla

Proceedings of the AMIA 2016, 2016

Link Prediction in a Semi-bipartite Network for Recommendation.

Aastha Nigam, Nitesh V. Chawla

Proceedings of the Intelligent Information and Database Systems - 8th Asian Conference, 2016

2015

Evaluating link prediction methods.

Yang Yang, Ryan N. Lichtenwalter, Nitesh V. Chawla

Knowl. Inf. Syst., 2015

Scaling and contextualizing personalized healthcare: A case study of disease prediction algorithm integration.

Keith Feldman, Darcy A. Davis, Nitesh V. Chawla

J. Biomed. Informatics, 2015

Modeling the Interplay Between Individual Behavior and Network Distributions.

Yang Yang, Jie Tang, Yuxiao Dong, Qiaozhu Mei, Reid A. Johnson, Nitesh V. Chawla

CoRR, 2015

Representing Higher Order Dependencies in Networks.

Jian Xu, Thanuka L. Wickramarathne, Nitesh V. Chawla

CoRR, 2015

Does Medical School Training Relate to Practice? Evidence from Big Data.

Keith Feldman, Nitesh V. Chawla

Big Data, 2015

Will This Paper Increase Your <i>h</i>-index?: Scientific Impact Prediction.

Yuxiao Dong, Reid A. Johnson, Nitesh V. Chawla

Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, 2015

On spatial-temporal truth finding in social sensing.

Chao Huang, Dong Wang, Nitesh V. Chawla

Proceedings of the 12th Annual IEEE International Conference on Sensing, 2015

Inferring Unusual Crowd Events from Mobile Phone Call Detail Records.

Yuxiao Dong, Fabio Pinelli, Yiannis Gkoufas, Zubair Nabi, Francesco Calabrese, Nitesh V. Chawla

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Will This Paper Increase Your h-index?

Yuxiao Dong, Reid A. Johnson, Nitesh V. Chawla

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

The Evolution of Social Relationships and Strategies Across the Lifespan.

Yuxiao Dong, Nitesh V. Chawla, Jie Tang, Yang Yang, Yang Yang

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Optimizing Classifiers for Hypothetical Scenarios.

Reid A. Johnson, Troy Raeder, Nitesh V. Chawla

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2015

Towards Time-Sensitive Truth Discovery in Social Sensing Applications.

Chao Huang, Dong Wang, Nitesh V. Chawla

Proceedings of the 12th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, 2015

Qualitatively exploring electronic portfolios: a text mining approach to measuring student emotion as an early warning indicator.

Frederick Nwanganga, Everaldo Aguiar, G. Alex Ambrose, Victoria Goodrich, Nitesh V. Chawla

Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, 2015

CoupledLP: Link Prediction in Coupled Networks.

Yuxiao Dong, Jing Zhang, Jie Tang, Nitesh V. Chawla, Bai Wang

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

Being a "Dataologist": From Data to Networks to Personalized Healthcare.

Nitesh V. Chawla

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (IFSA-EUSFLAT-15), 2015

Predicting online video engagement using clickstreams.

Everaldo Aguiar, Saurabh Nagrecha, Nitesh V. Chawla

Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, 2015

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline.

Keith Feldman, Louis Faust, Xian Wu, Chao Huang, Nitesh V. Chawla

Proceedings of the Towards Integrative Machine Learning and Knowledge Extraction, 2015

Recurrent Subgraph Prediction.

Saurabh Nagrecha, Nitesh V. Chawla, Horst Bunke

Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2015

Collaboration Signatures Reveal Scientific Impact.

Yuxiao Dong, Reid A. Johnson, Yang Yang, Nitesh V. Chawla

Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2015

2014

Engagement vs Performance: Using Electronic Portfolios to Predict First Semester Engineering Student Persistence.

Everaldo Aguiar, G. Alex Ambrose, Nitesh V. Chawla, Victoria Goodrich, Jay B. Brockman

J. Learn. Anal., November, 2014

Link Prediction: A Primer.

Nitesh V. Chawla, Yang Yang

Encyclopedia of Social Network Analysis and Mining, 2014

Convergence Analysis of Iterated Belief Revision in Complex Fusion Environments.

Thanuka L. Wickramarathne, Kamal Premaratne, Manohar N. Murthi, Nitesh V. Chawla

IEEE J. Sel. Top. Signal Process., 2014

A supervised learning approach to the ensemble clustering of genes.

Andrew K. Rider, Geoffrey Siwo, Scott J. Emrich, Michael T. Ferdig, Nitesh V. Chawla

Int. J. Data Min. Bioinform., 2014

Predicting Node Degree Centrality with the Node Prominence Profile.

Yang Yang, Yuxiao Dong, Nitesh V. Chawla

CoRR, 2014

Patterns of Ship-borne Species Spread: A Clustering Approach for Risk Assessment and Management of Non-indigenous Species Spread.

Jian Xu, Thanuka L. Wickramarathne, Erin K. Grey, Karsten Steinhaeuser, Reuben P. Keller, John M. Drake, Nitesh V. Chawla, David M. Lodge

CoRR, 2014

Inferring social status and rich club effects in enterprise communication networks.

Yuxiao Dong, Jie Tang, Nitesh V. Chawla, Tiancheng Lou, Yang Yang, Bai Wang

CoRR, 2014

Will This Paper Increase Your h-index? Scientific Impact Prediction.

Yuxiao Dong, Reid A. Johnson, Nitesh V. Chawla

CoRR, 2014

Engagement vs performance: using electronic portfolios to predict first semester engineering student retention.

Everaldo Aguiar, Nitesh V. Chawla, Jay B. Brockman, G. Alex Ambrose, Victoria Goodrich

Proceedings of the Learning Analytics and Knowledge Conference 2014, 2014

Improving management of aquatic invasions by integrating shipping network, ecological, and environmental data: data mining for social good.

Jian Xu, Thanuka L. Wickramarathne, Nitesh V. Chawla, Erin K. Grey, Karsten Steinhaeuser, Reuben P. Keller, John M. Drake, David M. Lodge

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

Inferring user demographics and social strategies in mobile social networks.

Yuxiao Dong, Yang Yang, Jie Tang, Yang Yang, Nitesh V. Chawla

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

Using HDDT to avoid instances propagation in unbalanced and evolving data streams.

Andrea Dal Pozzolo, Reid A. Johnson, Olivier Caelen, Serge Waterschoot, Nitesh V. Chawla, Gianluca Bontempi

Proceedings of the 2014 International Joint Conference on Neural Networks, 2014

Admission duration model for infant treatment (ADMIT).

Keith Feldman, Nitesh V. Chawla

Proceedings of the 2014 IEEE International Conference on Bioinformatics and Biomedicine, 2014

An integrated and digitized care framework for successful aging.

Dipanwita Dasgupta, Keith Feldman, Disha Waghray, W. A. Mikels-Carrasco, Patty Willaert, Debra A. Raybold, Nitesh V. Chawla

Proceedings of IEEE-EMBS International Conference on Biomedical and Health Informatics, 2014

2013

Perspective on Measurement Metrics for Community Detection Algorithms.

Yang Yang, Yizhou Sun, Saurav Pandit, Nitesh V. Chawla, Jiawei Han

Proceedings of the Mining Social Networks and Security Informatics, 2013

Reliable medical recommendation systems with patient privacy.

T. Ryan Hoens, Marina Blanton, Aaron Steele, Nitesh V. Chawla

ACM Trans. Intell. Syst. Technol., 2013

Supervised methods for multi-relational link prediction.

Darcy A. Davis, Ryan Lichtenwalter, Nitesh V. Chawla

Soc. Netw. Anal. Min., 2013

Special Issue on CIDU '11.

Ashok N. Srivastava, Nitesh V. Chawla

Stat. Anal. Data Min., 2013

Microscopic Evolution of Social Networks by Triad Position Profile.

Yang Yang, Yuxiao Dong, Nitesh V. Chawla

CoRR, 2013

How Long Will She Call Me? Distribution, Social Theory and Duration Prediction.

Yuxiao Dong, Jie Tang, Tiancheng Lou, Bin Wu, Nitesh V. Chawla

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

Prominence in networks: A co-evolving process.

Yang Yang, Nitesh V. Chawla, Xiaohui Lu, Sibel Adali

Proceedings of the 2nd IEEE Network Science Workshop, 2013

Red Black Network: Temporal and Topological Analysis of Two Intertwined Social Networks.

Saurav Pandit, Jonathan Koch, Yang Yang, Brian Uzzi, Nitesh V. Chawla

Proceedings of the 32th IEEE Military Communications Conference, 2013

Classifier Evaluation with Missing Negative Class Labels.

Andrew K. Rider, Reid A. Johnson, Darcy A. Davis, T. Ryan Hoens, Nitesh V. Chawla

Proceedings of the Advances in Intelligent Data Analysis XII, 2013

An Ensemble Topic Model for Sharing Healthcare Data and Predicting Disease Risk.

Andrew K. Rider, Nitesh V. Chawla

Proceedings of the ACM Conference on Bioinformatics, 2013

Link prediction in human mobility networks.

Yang Yang, Nitesh V. Chawla, Prithwish Basu, Bhaskar Prabhala, Thomas La Porta

Proceedings of the Advances in Social Networks Analysis and Mining 2013, 2013

Comparison of Gene Co-expression Networks and Bayesian Networks.

Saurabh Nagrecha, Pawan Lingras, Nitesh V. Chawla

Proceedings of the Intelligent Information and Database Systems - 5th Asian Conference, 2013

2012

A unifying view on dataset shift in classification.

Jose G. Moreno-Torres, Troy Raeder, Rocío Alaíz-Rodríguez, Nitesh V. Chawla, Francisco Herrera

Pattern Recognit., 2012

Learning from streaming data with concept drift and imbalance: an overview.

T. Ryan Hoens, Robi Polikar, Nitesh V. Chawla

Prog. Artif. Intell., 2012

Hellinger distance decision trees are robust and skew-insensitive.

David A. Cieslak, T. Ryan Hoens, Nitesh V. Chawla, W. Philip Kegelmeyer

Data Min. Knowl. Discov., 2012

Vertex collocation profiles: subgraph counting for link analysis and prediction.

Ryan Lichtenwalter, Nitesh V. Chawla

Proceedings of the 21st World Wide Web Conference 2012, 2012

When will it happen?: relationship prediction in heterogeneous information networks.

Yizhou Sun, Jiawei Han, Charu C. Aggarwal, Nitesh V. Chawla

Proceedings of the Fifth International Conference on Web Search and Web Data Mining, 2012

ALIVE: A Multi-relational Link Prediction Environment for the Healthcare Domain.

Reid A. Johnson, Yang Yang, Everaldo Aguiar, Andrew K. Rider, Nitesh V. Chawla

Proceedings of the Emerging Trends in Knowledge Discovery and Data Mining - PAKDD 2012 International Workshops: DMHM, GeoDoc, 3Clust, and DSDM, Kuala Lumpur, Malaysia, May 29, 2012

Building Decision Trees for the Multi-class Imbalance Problem.

T. Ryan Hoens, Qi Qian, Nitesh V. Chawla, Zhi-Hua Zhou

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2012

Learning in non-stationary environments with class imbalance.

Thomas Ryan Hoens, Nitesh V. Chawla

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012

Predicting Links in Multi-relational and Heterogeneous Networks.

Yang Yang, Nitesh V. Chawla, Yizhou Sun, Jiawei Han

Proceedings of the 12th IEEE International Conference on Data Mining, 2012

Maximizing Information Spread through Influence Structures in Social Networks.

Saurav Pandit, Yang Yang, Nitesh V. Chawla

Proceedings of the 12th IEEE International Conference on Data Mining Workshops, 2012

Link Prediction and Recommendation across Heterogeneous Social Networks.

Yuxiao Dong, Jie Tang, Sen Wu, Jilei Tian, Nitesh V. Chawla, Jinghai Rao, Huanhuan Cao

Proceedings of the 12th IEEE International Conference on Data Mining, 2012

Species distribution modeling and prediction: A class imbalance problem.

Reid A. Johnson, Nitesh V. Chawla, Jessica J. Hellmann

Proceedings of the 2012 Conference on Intelligent Data Understanding, 2012

Foreword.

Nitesh V. Chawla, Ashok N. Srivastava

Proceedings of the 2012 Conference on Intelligent Data Understanding, 2012

Link Prediction: Fair and Effective Evaluation.

Ryan Lichtenwalter, Nitesh V. Chawla

Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, 2012

2011

Predictors of short-term decay of cell phone contacts in a large scale communication network.

Troy Raeder, Omar Lizardo, David Hachen, Nitesh V. Chawla

Soc. Networks, 2011

Market basket analysis with networks.

Troy Raeder, Nitesh V. Chawla

Soc. Netw. Anal. Min., 2011

Complex networks as a unified framework for descriptive analysis and predictive modeling in climate science.

Karsten Steinhaeuser, Nitesh V. Chawla, Auroop R. Ganguly

Stat. Anal. Data Min., 2011

Special issue on the best papers of the Conference on Intelligent Data Understanding (CIDU 2010).

Ashok N. Srivastava, Nitesh V. Chawla

Stat. Anal. Data Min., 2011

LPmade: Link Prediction Made Easy.

Ryan Lichtenwalter, Nitesh V. Chawla

J. Mach. Learn. Res., 2011

Range-limited Centrality Measures in Complex Networks

Mária Ercsey-Ravasz, Ryan Lichtenwalter, Nitesh V. Chawla, Zoltán Toroczkai

CoRR, 2011

Weighted reciprocity in human communication networks

Nitesh V. Chawla, David Hachen, Omar Lizardo, Zoltán Toroczkai, Anthony Strathman, Cheng Wang

CoRR, 2011

Comparing Predictive Power in Climate Data: Clustering Matters.

Karsten Steinhaeuser, Nitesh V. Chawla, Auroop R. Ganguly

Proceedings of the Advances in Spatial and Temporal Databases, 2011

Heuristic Updatable Weighted Random Subspaces for Non-stationary Environments.

T. Ryan Hoens, Nitesh V. Chawla, Robi Polikar

Proceedings of the 11th IEEE International Conference on Data Mining, 2011

Network Effects on Tweeting.

Jake T. Lussier, Nitesh V. Chawla

Proceedings of the Discovery Science - 14th International Conference, 2011

Empirical comparison of correlation measures and pruning levels in complex networks representing the global climate system.

Alex Pelan, Karsten Steinhaeuser, Nitesh V. Chawla, Dilkushi A. de Alwis Pitts, Auroop R. Ganguly

Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, 2011

Is Objective Function the Silver Bullet? A Case Study of Community Detection Algorithms on Social Networks.

Yang Yang, Yizhou Sun, Saurav Pandit, Nitesh V. Chawla, Jiawei Han

Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, 2011

DisNet: A Framework for Distributed Graph Computation.

Ryan Lichtenwalter, Nitesh V. Chawla

Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, 2011

Multi-relational Link Prediction in Heterogeneous Information Networks.

Darcy A. Davis, Ryan Lichtenwalter, Nitesh V. Chawla

Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, 2011

2010

Data Mining for Imbalanced Datasets: An Overview.

Nitesh V. Chawla

Proceedings of the Data Mining and Knowledge Discovery Handbook, 2nd ed., 2010

An exploration of climate data using complex networks.

Karsten Steinhaeuser, Nitesh V. Chawla, Auroop R. Ganguly

SIGKDD Explor., 2010

Knowledge discovery from sensor data (SensorKDD).

Varun Chandola, Olufemi A. Omitaomu, Auroop R. Ganguly, Ranga Raju Vatsavai, Nitesh V. Chawla, João Gama, Mohamed Medhat Gaber

SIGKDD Explor., 2010

Identifying and evaluating community structure in complex networks.

Karsten Steinhaeuser, Nitesh V. Chawla

Pattern Recognit. Lett., 2010

A Machine-Learning Approach to Autonomous Music Composition.

Ryan Lichtenwalter, Katerina Lichtenwalter, Nitesh V. Chawla

J. Intell. Syst., 2010

Time to CARE: a collaborative engine for practical disease prediction.

Darcy A. Davis, Nitesh V. Chawla, Nicholas A. Christakis, Albert-László Barabási

Data Min. Knowl. Discov., 2010

A statistical approach to finding overlooked genetic associations.

Andrew K. Rider, Geoffrey Siwo, Nitesh V. Chawla, Michael T. Ferdig, Scott J. Emrich

BMC Bioinform., 2010

Visualizing graph dynamics and similarity for enterprise network security and management.

Qi Liao, Aaron Striegel, Nitesh V. Chawla

Proceedings of the 7th International Symposium on Visualization for Cyber Security, 2010

A Private and Reliable Recommendation System for Social Networks.

T. Ryan Hoens, Marina Blanton, Nitesh V. Chawla

Proceedings of the 2010 IEEE Second International Conference on Social Computing, 2010

A Robust Decision Tree Algorithm for Imbalanced Data Sets.

Wei Liu, Sanjay Chawla, David A. Cieslak, Nitesh V. Chawla

Proceedings of the SIAM International Conference on Data Mining, 2010

User Generated Content Consumption and Social Networking in Knowledge-Sharing OSNs.

Jake T. Lussier, Troy Raeder, Nitesh V. Chawla

Proceedings of the Advances in Social Computing, 2010

Privacy-Preserving Network Aggregation.

Troy Raeder, Marina Blanton, Nitesh V. Chawla, Keith B. Frikken

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2010

Generating Diverse Ensembles to Counter the Problem of Class Imbalance.

T. Ryan Hoens, Nitesh V. Chawla

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2010

New perspectives and methods in link prediction.

Ryan Lichtenwalter, Jake T. Lussier, Nitesh V. Chawla

Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010

Reliable medical recommendation systems with patient privacy.

T. Ryan Hoens, Marina Blanton, Nitesh V. Chawla

Proceedings of the ACM International Health Informatics Symposium, 2010

HealthCareND: leveraging EHR and CARE for prospective healthcare.

Brian Dentino, Darcy A. Davis, Nitesh V. Chawla

Proceedings of the ACM International Health Informatics Symposium, 2010

An Incremental Learning Algorithm for Non-stationary Environments and Class Imbalance.

Gregory Ditzler, Robi Polikar, Nitesh V. Chawla

Proceedings of the 20th International Conference on Pattern Recognition, 2010

Consequences of Variability in Classifier Performance Estimates.

Troy Raeder, T. Ryan Hoens, Nitesh V. Chawla

Proceedings of the ICDM 2010, 2010

Digging up the Dirt on User Generated Content Consumption.

Jake T. Lussier, Troy Raeder, Nitesh V. Chawla

Proceedings of the 16th International Conference on Management of Data, 2010

Complex Networks In Climate Science: Progress, Opportunities And Challenges.

Karsten Steinhaeuser, Nitesh V. Chawla, Auroop R. Ganguly

Proceedings of the 2010 Conference on Intelligent Data Understanding, 2010

A supervised learning approach to the unsupervised clustering of genes.

Andrew K. Rider, Geoffrey Siwo, Scott J. Emrich, Michael T. Ferdig, Nitesh V. Chawla

Proceedings of the 2010 IEEE International Conference on Bioinformatics and Biomedicine, 2010

2009

SVMs Modeling for Highly Imbalanced Classification.

Yuchun Tang, Yan-Qing Zhang, Nitesh V. Chawla, Sven Krasser

IEEE Trans. Syst. Man Cybern. Part B, 2009

Knowledge discovery from sensor data (SensorKDD).

Olufemi A. Omitaomu, Ranga Raju Vatsavai, Auroop R. Ganguly, Nitesh V. Chawla, João Gama, Mohamed Medhat Gaber

SIGKDD Explor., 2009

A framework for monitoring classifiers' performance: when and why failure occurs?

David A. Cieslak, Nitesh V. Chawla

Knowl. Inf. Syst., 2009

Model Monitor (<i>M</i>²): Evaluating, Comparing, and Monitoring Models.

Troy Raeder, Nitesh V. Chawla

J. Mach. Learn. Res., 2009

Countering imbalanced datasets to improve adverse drug event predictive models in labor and delivery.

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

J. Biomed. Informatics, 2009

Adaptive Methods for Classification in Arbitrarily Imbalanced and Drifting Data Streams.

Ryan Lichtenwalter, Nitesh V. Chawla

Proceedings of the New Frontiers in Applied Data Mining, 2009

Mining in a mobile environment.

Sean McRoskey, James Notwell, Nitesh V. Chawla, Christian Poellabauer

Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data, 2009

Applying Learning Algorithms to Music Generation.

Ryan Lichtenwalter, Katerina Lichtenwalter, Nitesh V. Chawla

Proceedings of the 4th Indian International Conference on Artificial Intelligence, 2009

Determination of Specificity Residues in Two Component Systems using Graphlets.

Faruck Morcos, Charles Lamanna, Nitesh V. Chawla, Jesús A. Izaguirre

Proceedings of the International Conference on Bioinformatics & Computational Biology, 2009

Modeling a Store's Product Space as a Social Network.

Troy Raeder, Nitesh V. Chawla

Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, 2009

2008

Using selective, short-term memory to improve resilience against DDoS exhaustion attacks.

Qi Liao, David A. Cieslak, Aaron Striegel, Nitesh V. Chawla

Secur. Commun. Networks, 2008

Automatically countering imbalance and its empirical relationship to cost.

Nitesh V. Chawla, David A. Cieslak, Lawrence O. Hall, Ajay Joshi

Data Min. Knowl. Discov., 2008

Learning Decision Trees for Unbalanced Data.

David A. Cieslak, Nitesh V. Chawla

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008

Analyzing PETs on Imbalanced Datasets When Training and Testing Class Distributions Differ.

David A. Cieslak, Nitesh V. Chawla

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2008

Data mining on the grid for the grid.

Nitesh V. Chawla, Douglas Thain, Ryan Lichtenwalter, David A. Cieslak

Proceedings of the 22nd IEEE International Symposium on Parallel and Distributed Processing, 2008

Scaling up Classifiers to Cloud Computers.

Christopher Moretti, Karsten Steinhaeuser, Douglas Thain, Nitesh V. Chawla

Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 2008

Start Globally, Optimize Locally, Predict Globally: Improving Performance on Imbalanced Data.

David A. Cieslak, Nitesh V. Chawla

Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 2008

Troubleshooting thousands of jobs on production grids using data mining techniques.

David A. Cieslak, Nitesh V. Chawla, Douglas Thain

Proceedings of the 9th IEEE/ACM International Conference on Grid Computing (Grid 2008), Tsukuba, Japan, September 29, 2008

Using Small Abstractions to Program Large Distributed Systems.

Douglas Thain, Christopher Moretti, Hoang Bui, Li Yu, Nitesh V. Chawla, Patrick J. Flynn

Proceedings of the Fourth International Conference on e-Science, 2008

Predicting individual disease risk based on medical history.

Darcy A. Davis, Nitesh V. Chawla, Nicholas Blumm, Nicholas A. Christakis, Albert-László Barabási

Proceedings of the 17th ACM Conference on Information and Knowledge Management, 2008

2007

Anomaly detection in a mobile communication network.

Alec Pawling, Nitesh V. Chawla, Gregory R. Madey

Comput. Math. Organ. Theory, 2007

Authentication anomaly detection: a case study on a virtual private network.

Michael J. Chapple, Nitesh V. Chawla, Aaron Striegel

Proceedings of the 3rd Annual ACM Workshop on Mining Network Data, 2007

Exploiting Diversity in Ensembles: Improving the Performance on Unbalanced Datasets.

Nitesh V. Chawla, Jared Sylvester

Proceedings of the Multiple Classifier Systems, 7th International Workshop, 2007

Detecting Fractures in Classifier Performance.

David A. Cieslak, Nitesh V. Chawla

Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), 2007

Enhanced Situational Awareness: Application of DDDAS Concepts to Emergency and Disaster Management.

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

Proceedings of the Computational Science, 2007

A Black-Box Approach to Query Cardinality Estimation.

Tanu Malik, Randal C. Burns, Nitesh V. Chawla

Proceedings of the Third Biennial Conference on Innovative Data Systems Research, 2007

Actively Exploring Creation of Face Space(s) for Improved Face Recognition.

Nitesh V. Chawla, Kevin W. Bowyer

Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence, 2007

2006

A study in machine learning from imbalanced data for sentence boundary detection in speech.

Yang Liu, Nitesh V. Chawla, Mary P. Harper, Elizabeth Shriberg, Andreas Stolcke

Comput. Speech Lang., 2006

Data management and query - Estimating query result sizes for proxy caching in scientific database federations.

Tanu Malik, Randal C. Burns, Nitesh V. Chawla, Alexander S. Szalay

Proceedings of the ACM/IEEE SC2006 Conference on High Performance Networking and Computing, 2006

Evaluation of Summarization Schemes for Learning in Streams.

Alec Pawling, Nitesh V. Chawla, Amitabh Chaudhary

Proceedings of the Knowledge Discovery in Databases: PKDD 2006, 2006

Evolutionary Ensemble Creation and Thinning.

Jared Sylvester, Nitesh V. Chawla

Proceedings of the International Joint Conference on Neural Networks, 2006

Troubleshooting Distributed Systems via Data Mining.

David A. Cieslak, Douglas Thain, Nitesh V. Chawla

Proceedings of the 15th IEEE International Symposium on High Performance Distributed Computing, 2006

Combating imbalance in network intrusion datasets.

David A. Cieslak, Nitesh V. Chawla, Aaron Striegel

Proceedings of the 2006 IEEE International Conference on Granular Computing, 2006

Information Gain, Correlation and Support Vector Machines.

Danny Roobaert, Grigoris I. Karakoulas, Nitesh V. Chawla

Proceedings of the Feature Extraction - Foundations and Applications, 2006

2005

Learning From Labeled And Unlabeled Data: An Empirical Study Across Techniques And Domains.

Nitesh V. Chawla, Grigoris I. Karakoulas

J. Artif. Intell. Res., 2005

Ensembles in face recognition: tackling the extremes of high dimensionality, temporality, and variance in data.

Nitesh V. Chawla, Kevin W. Bowyer

Proceedings of the IEEE International Conference on Systems, 2005

Many Are Better Than One: Improving Probabilistic Estimates from Decision Trees.

Nitesh V. Chawla

Proceedings of the Machine Learning Challenges, 2005

Designing Multiple Classifier Systems for Face Recognition.

Nitesh V. Chawla, Kevin W. Bowyer

Proceedings of the Multiple Classifier Systems, 6th International Workshop, 2005

Random Subspaces and Subsampling for 2-D Face Recognition.

Nitesh V. Chawla, Kevin W. Bowyer

Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), 2005

Data Mining for Imbalanced Datasets: An Overview.

Nitesh V. Chawla

Proceedings of the Data Mining and Knowledge Discovery Handbook., 2005

2004

Editorial: special issue on learning from imbalanced data sets.

Nitesh V. Chawla, Nathalie Japkowicz, Aleksander Kotcz

SIGKDD Explor., 2004

Learning Ensembles from Bites: A Scalable and Accurate Approach.

Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer

J. Mach. Learn. Res., 2004

Classification and knowledge discovery in protein databases.

Predrag Radivojac, Nitesh V. Chawla, A. Keith Dunker, Zoran Obradovic

J. Biomed. Informatics, 2004

2003

Distributed learning with bagging-like performance.

Nitesh V. Chawla, Thomas E. Moore, Lawrence O. Hall, Kevin W. Bowyer, W. Philip Kegelmeyer, Clayton Springer

Pattern Recognit. Lett., 2003

SMOTEBoost: Improving Prediction of the Minority Class in Boosting.

Nitesh V. Chawla, Aleksandar Lazarevic, Lawrence O. Hall, Kevin W. Bowyer

Proceedings of the Knowledge Discovery in Databases: PKDD 2003, 2003

2002

SMOTE: Synthetic Minority Over-sampling Technique.

Nitesh V. Chawla, Kevin W. Bowyer, Lawrence O. Hall, W. Philip Kegelmeyer

J. Artif. Intell. Res., 2002

Distributed Pasting of Small Votes.

Nitesh V. Chawla, Lawrence O. Hall, Kevin W. Bowyer, Thomas E. Moore, W. Philip Kegelmeyer

Proceedings of the Multiple Classifier Systems, Third International Workshop, 2002

Generalization Methods in Bioinformatics.

Steven Eschrich, Nitesh V. Chawla, Lawrence O. Hall

Proceedings of the 2nd ACM SIGKDD Workshop on Data Mining in Bioinformatics (BIOKDD 2002), 2002

2001

Investigation of bagging-like effects and decision trees versus neural nets in protein secondary structure prediction.

Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, W. Philip Kegelmeyer

Proceedings of the ACM SIGKDD Workshop on Data Mining in Bioinformatics (BIOKDD 2001), 2001

Creating Ensembles of Classifiers.

Nitesh V. Chawla, Steven Eschrich, Lawrence O. Hall

Proceedings of the 2001 IEEE International Conference on Data Mining, 29 November, 2001

Bagging Is a Small-Data-Set Phenomenon.

Nitesh V. Chawla, Thomas E. Moore, Kevin W. Bowyer, Lawrence O. Hall, Clayton Springer, W. Philip Kegelmeyer

Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), 2001

2000

A parallel decision tree builder for mining very large visualization datasets.

Kevin W. Bowyer, Lawrence O. Hall, Thomas Moore, Nitesh V. Chawla, W. Philip Kegelmeyer

Proceedings of the IEEE International Conference on Systems, 2000

1999

Learning Rules from Distributed Data.

Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowyer, W. Philip Kegelmeyer

Proceedings of the Large-Scale Parallel Data Mining, 1999

1998

Decision tree learning on very large data sets.

Lawrence O. Hall, Nitesh V. Chawla, Kevin W. Bowyer

Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 1998