| 2024 | KDD | Unraveling Block Maxima Forecasting Models with Counterfactual Explanation. | Yue Deng, Asadullah Hill Galib, Pang-Ning Tan, Lifeng Luo |
| 2023 | ICDM | SimEXT: Self-supervised Representation Learning for Extreme Values in Time Series. | Asadullah Hill Galib, Pang-Ning Tan, Lifeng Luo |
| 2023 | ICDM | Influence Propagation for Linear Threshold Model with Graph Neural Networks. | Francisco Santos, Anna Stephens, Pang-Ning Tan, Abdol-Hossein Esfahanian |
| 2023 | ICDM | Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groups. | Anna Stephens, Francisco Santos, Pang-Ning Tan, Abdol-Hossein Esfahanian |
| 2023 | IJCAI | Self-Recover: Forecasting Block Maxima in Time Series from Predictors with Disparate Temporal Coverage Using Self-Supervised Learning. | Asadullah Hill Galib, Andrew McDonald, Pang-Ning Tan, Lifeng Luo |
| 2022 | AAAI | Unsupervised Anomaly Detection by Robust Density Estimation. | Boyang Liu, Pang-Ning Tan, Jiayu Zhou |
| 2022 | AAAI | DeepGPD: A Deep Learning Approach for Modeling Geospatio-Temporal Extreme Events. | Tyler Wilson, Pang-Ning Tan, Lifeng Luo |
| 2022 | ICDM | Fairness-Aware Graph Sampling for Network Analysis. | Farzan Masrour, Francisco Santos, Pang-Ning Tan, Abdol-Hossein Esfahanian |
| 2022 | IJCAI | DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data. | Asadullah Hill Galib, Andrew McDonald, Tyler Wilson, Lifeng Luo, Pang-Ning Tan |
| 2022 | IJCAI | COMET Flows: Towards Generative Modeling of Multivariate Extremes and Tail Dependence. | Andrew McDonald, Pang-Ning Tan, Lifeng Luo |
| 2022 | IJCNN | FACS-GCN: Fairness-Aware Cost-Sensitive Boosting of Graph Convolutional Networks. | Francisco Santos, Junke Ye, Farzan Masrour, Pang-Ning Tan, Abdol-Hossein Esfahanian |
| 2022 | KDD | Beyond Point Prediction: Capturing Zero-Inflated & Heavy-Tailed Spatiotemporal Data with Deep Extreme Mixture Models. | Tyler Wilson, Andrew McDonald, Asadullah Hill Galib, Pang-Ning Tan, Lifeng Luo |
| 2021 | ICML | Learning Deep Neural Networks under Agnostic Corrupted Supervision. | Boyang Liu, Mengying Sun, Ding Wang, Pang-Ning Tan, Jiayu Zhou |
| 2021 | IJCAI | RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection. | Boyang Liu, Ding Wang, Kaixiang Lin, Pang-Ning Tan, Jiayu Zhou |
| 2021 | KDD | JOHAN: A Joint Online Hurricane Trajectory and Intensity Forecasting Framework. | Ding Wang, Pang-Ning Tan |
| 2020 | AAAI | Bursting the Filter Bubble: Fairness-Aware Network Link Prediction. | Farzan Masrour, Tyler Wilson, Heng Yan, Pang-Ning Tan, Abdol-Hossein Esfahanian |
| 2020 | AAAI | OMuLeT: Online Multi-Lead Time Location Prediction for Hurricane Trajectory Forecasting. | Ding Wang, Boyang Liu, Pang-Ning Tan, Lifeng Luo |
| 2020 | ICDM | Fairness Perception from a Network-Centric Perspective. | Farzan Masrour, Pang-Ning Tan, Abdol-Hossein Esfahanian |
| 2020 | SDM | Convolutional Methods for Predictive Modeling of Geospatial Data. | Tyler Wilson, Pang-Ning Tan, Lifeng Luo |
| 2019 | DSAA | Hierarchical LSTM Framework for Long-Term Sea Surface Temperature Forecasting. | Xi Liu, Tyler Wilson, Pang-Ning Tan, Lifeng Luo |
| 2019 | SDM | Augmented Multi-Task Learning by Optimal Transport. | Boyang Liu, Pang-Ning Tan, Jiayu Zhou |
| 2019 | SDM | Deep Multi-view Information Bottleneck. | Qi Wang, Claire Boudreau, Qixing Luo, Pang-Ning Tan, Jiayu Zhou |
| 2018 | ICDM | Distribution Preserving Multi-task Regression for Spatio-Temporal Data. | Xi Liu, Pang-Ning Tan, Zubin Abraham, Lifeng Luo, Pouyan Hatami |
| 2018 | ICDM | Imputing Structured Missing Values in Spatial Data with Clustered Adversarial Matrix Factorization. | Qi Wang, Pang-Ning Tan, Jiayu Zhou |
| 2018 | ICDM | A Low Rank Weighted Graph Convolutional Approach to Weather Prediction. | Tyler Wilson, Pang-Ning Tan, Lifeng Luo |
| 2018 | IJCAI | MUSCAT: Multi-Scale Spatio-Temporal Learning with Application to Climate Modeling. | Jianpeng Xu, Xi Liu, Tyler Wilson, Pang-Ning Tan, Pouyan Hatami, Lifeng Luo |
| 2018 | KDD | Enhancing Predictive Modeling of Nested Spatial Data through Group-Level Feature Disaggregation. | Boyang Liu, Pang-Ning Tan, Jiayu Zhou |
| 2018 | PAKDD | STARS: Soft Multi-Task Learning for Activity Recognition from Multi-Modal Sensor Data. | Xi Liu, Pang-Ning Tan, Lei Liu |
| 2017 | ICDM | Multi-level Multi-task Learning for Modeling Cross-Scale Interactions in Nested Geospatial Data. | Shuai Yuan, Jiayu Zhou, Pang-Ning Tan, C. Emi Fergus, Tyler Wagner, Patricia A. Soranno |
| 2017 | SDM | Hash-Based Feature Learning for Incomplete Continuous-Valued Data. | Shuai Yuan, Pang-Ning Tan, Kendra Spence Cheruvelil, C. Emi Fergus, Nicholas K. Skaff, Patricia A. Soranno |
| 2016 | IJCNN | Crowdsourcing of network data. | Ding Wang, Prakash Mandayam Comar, Pang-Ning Tan |
| 2016 | INFOCOM | Macro-scale mobile app market analysis using customized hierarchical categorization. | Xi Liu, Han Hee Song, Mario Baldi, Pang-Ning Tan |
| 2016 | SDM | Synergies that Matter: Efficient Interaction Selection via Sparse Factorization Machine. | Jianpeng Xu, Kaixiang Lin, Pang-Ning Tan, Jiayu Zhou |
| 2016 | SDM | GSpartan: a Geospatio-Temporal Multi-task Learning Framework for Multi-location Prediction. | Jianpeng Xu, Pang-Ning Tan, Lifeng Luo, Jiayu Zhou |
| 2015 | CIKM | MF-Tree: Matrix Factorization Tree for Large Multi-Class Learning. | Lei Liu, Pang-Ning Tan, Xi Liu |
| 2015 | DSAA | Constrained spectral clustering for regionalization: Exploring the trade-off between spatial contiguity and landscape homogeneity. | Shuai Yuan, Pang-Ning Tan, Kendra Spence Cheruvelil, Sarah M. Collins, Patricia A. Soranno |
| 2015 | SDM | FORMULA: FactORized MUlti-task LeArning for task discovery in personalized medical models. | Jianpeng Xu, Jiayu Zhou, Pang-Ning Tan |
| 2014 | ICDM | ORION: Online Regularized Multi-task Regression and Its Application to Ensemble Forecasting. | Jianpeng Xu, Pang-Ning Tan, Lifeng Luo |
| 2013 | INFOCOM | Combining supervised and unsupervised learning for zero-day malware detection. | Prakash Mandayam Comar, Lei Liu, Sabyasachi Saha, Pang-Ning Tan, Antonio Nucci |
| 2013 | SDM | Distribution Regularized Regression Framework for Climate Modeling. | Zubin Abraham, Malgorzata Liszewska, Perdinan, Pang-Ning Tan, Julie Winkler, Shiyuan Zhong |
| 2013 | SDM | Missing or Inapplicable: Treatment of Incomplete Continuous-valued Features in Supervised Learning. | Lei Liu, Prakash Mandayam Comar, Antonio Nucci, Sabyasachi Saha, Pang-Ning Tan |
| 2012 | CIKM | Weighted linear kernel with tree transformed features for malware detection. | Prakash Mandayam Comar, Lei Liu, Sabyasachi Saha, Antonio Nucci, Pang-Ning Tan |
| 2012 | ICPR | Recursive NMF: Efficient label tree learning for large multi-class problems. | Lei Liu, Prakash Mandayam Comar, Sabyasachi Saha, Pang-Ning Tan, Antonio Nucci |
| 2011 | ICDM | LinkBoost: A Novel Cost-Sensitive Boosting Framework for Community-Level Network Link Prediction. | Prakash Mandayam Comar, Pang-Ning Tan, Anil K. Jain |
| 2011 | KDD | Detecting bots via incremental LS-SVM learning with dynamic feature adaptation. | Feilong Chen, Supranamaya Ranjan, Pang-Ning Tan |
| 2010 | CIKM | Multi task learning on multiple related networks. | Prakash Mandayam Comar, Pang-Ning Tan, Anil Kumar Jain |
| 2010 | SDM | An Integrated Framework for Simultaneous Classification and Regression of Time-Series Data. | Zubin Abraham, Pang-Ning Tan |
| 2009 | CIKM | A co-classification framework for detecting web spam and spammers in social media web sites. | Feilong Chen, Pang-Ning Tan, Anil K. Jain |
| 2009 | ICDM | A Semi-supervised Framework for Simultaneous Classification and Regression of Zero-Inflated Time Series Data with Application to Precipitation Prediction. | Zubin Abraham, Pang-Ning Tan |
| 2009 | KDD | Measuring the effects of preprocessing decisions and network forces in dynamic network analysis. | Jerry Scripps, Pang-Ning Tan, Abdol-Hossein Esfahanian |
| 2009 | SAC | Combining statistics and semantics via ensemble model for document clustering. | Samah Jamal Fodeh, William F. Punch, Pang-Ning Tan |
| 2009 | SDM | Detection and Characterization of Anomalies in Multivariate Time Series. | Haibin Cheng, Pang-Ning Tan, Christopher Potter, Steven A. Klooster |
| 2008 | ICDM | A Robust Graph-Based Algorithm for Detection and Characterization of Anomalies in Noisy Multivariate Time Series. | Haibin Cheng, Pang-Ning Tan, Christopher Potter, Steven A. Klooster |
| 2008 | ICPR | A matrix alignment approach for link prediction. | Jerry Scripps, Pang-Ning Tan, Feilong Chen, Abdol-Hossein Esfahanian |
| 2008 | KDD | Semi-supervised learning with data calibration for long-term time series forecasting. | Haibin Cheng, Pang-Ning Tan |
| 2007 | CIDM | A Prototype-driven Framework for Change Detection in Data Stream Classification. | Hamed Valizadegan, Pang-Ning Tan |
| 2007 | ICDM | Recommendation via Query Centered Random Walk on K-Partite Graph. | Haibin Cheng, Pang-Ning Tan, Jon Sticklen, William F. Punch |
| 2007 | ICDM | Exploration of Link Structure and Community-Based Node Roles in Network Analysis. | Jerry Scripps, Pang-Ning Tan, Abdol-Hossein Esfahanian |
| 2007 | ICTAI | Incorporating Background Knowledge for Subjective Rule Evaluation. | Samah Jamal Fodeh, Pang-Ning Tan |
| 2007 | ICTAI | A Probabilistic Substructure-Based Approach for Graph Classification. | H. D. K. Moonesinghe, Hamed Valizadegan, Samah Jamal Fodeh, Pang-Ning Tan |
| 2007 | SDM | Localized Support Vector Machine and Its Efficient Algorithm. | Haibin Cheng, Pang-Ning Tan, Rong Jin |
| 2007 | SDM | Kernel Based Detection of Mislabeled Training Examples. | Hamed Valizadegan, Pang-Ning Tan |
| 2006 | ICDM | Converting Output Scores from Outlier Detection Algorithms into Probability Estimates. | Jing Gao, Pang-Ning Tan |
| 2006 | ICDM | Frequent Closed Itemset Mining Using Prefix Graphs with an Efficient Flow-Based Pruning Strategy. | H. D. K. Moonesinghe, Samah Jamal Fodeh, Pang-Ning Tan |
| 2006 | ICPP | Adaptively Routing P2P Queries Using Association Analysis. | Brian D. Connelly, Christopher W. Bowron, Li Xiao, Pang-Ning Tan, Chen Wang |
| 2006 | ICTAI | Outlier Detection Using Random Walks. | H. D. K. Moonesinghe, Pang-Ning Tan |
| 2006 | PAKDD | Multistep-Ahead Time Series Prediction. | Haibin Cheng, Pang-Ning Tan, Jing Gao, Jerry Scripps |
| 2006 | SAC | Semi-supervised outlier detection. | Jing Gao, Haibin Cheng, Pang-Ning Tan |
| 2006 | SDM | A Novel Framework for Incorporating Labeled Examples into Anomaly Detection. | Jing Gao, Haibin Cheng, Pang-Ning Tan |
| 2006 | SDM | Semi-Supervised Clustering with Partial Background Information. | Jing Gao, Pang-Ning Tan, Haibin Cheng |
| 2006 | SDM | Clustering in the Presence of Bridge-Nodes. | Jerry Scripps, Pang-Ning Tan |
| 2005 | PAKDD | An Incremental Data Stream Clustering Algorithm Based on Dense Units Detection. | Jing Gao, Jianzhong Li, Zhaogong Zhang, Pang-Ning Tan |
| 2004 | ICMLA | Mining interesting contrast rules for a web-based educational system. | Behrouz Minaei-Bidgoli, Pang-Ning Tan, William F. Punch |
| 2004 | KDD | Support envelopes: a technique for exploring the structure of association patterns. | Michael S. Steinbach, Pang-Ning Tan, Vipin Kumar |
| 2004 | KDD | Generalizing the notion of support. | Michael S. Steinbach, Pang-Ning Tan, Hui Xiong, Vipin Kumar |
| 2004 | KDD | Ordering patterns by combining opinions from multiple sources. | Pang-Ning Tan, Rong Jin |
| 2004 | KDD | Exploiting a support-based upper bound of Pearson's correlation coefficient for efficiently identifying strongly correlated pairs. | Hui Xiong, Shashi Shekhar, Pang-Ning Tan, Vipin Kumar |
| 2004 | SDM | RBA: An Integrated Framework for Regression based on Association Rules. | Aysel Ozgur, Pang-Ning Tan, Vipin Kumar |
| 2004 | SDM | HICAP: Hierarchical Clustering with Pattern Preservation. | Hui Xiong, Michael S. Steinbach, Pang-Ning Tan, Vipin Kumar |
| 2003 | ICDM | Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution. | Hui Xiong, Pang-Ning Tan, Vipin Kumar |
| 2003 | KDD | Discovery of climate indices using clustering. | Michael S. Steinbach, Pang-Ning Tan, Vipin Kumar, Steven A. Klooster, Christopher Potter |
| 2002 | KDD | Selecting the right interestingness measure for association patterns. | Pang-Ning Tan, Vipin Kumar, Jaideep Srivastava |
| 2001 | KDD | Mining Indirect Associations in Web Data. | Pang-Ning Tan, Vipin Kumar |
| 2000 | KDD | Textual data mining of service center call records. | Pang-Ning Tan, Hannah Blau, Steven A. Harp, Robert P. Goldman |
| 1999 | KDD | Discovery of Interesting Usage Patterns from Web Data. | Robert Cooley, Pang-Ning Tan, Jaideep Srivastava |