2024

• Wenxuan Bao, Jun Wu, and Jingrui He. BOBA: Byzantine-Robust Federated Learning with Label Skewness. The 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024).
• Dongqi Fu, Zhigang Hua, Yan Xie, Jin Fang, Si Zhang, Kaan Sancak, Hao Wu, Andrey Malevich, Jingrui He, and Bo Long. VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections. The 12th International Conference on Learning Representations (ICLR 2024).
• Tianxin Wei, Bowen Jin, Ruirui Li, Hansi Zeng, Zhengyang Wang, Jianhui Sun, Qingyu Yin, Hanqing Lu, Suhang Wang, Jingrui He, Xianfeng Tang. Towards Universal Multi-Modal Personalization: A Language Model Empowered Generative Paradigm. The 12th International Conference on Learning Representations (ICLR 2024).
• Yikun Ban, Ishika Agarwal, Ziwei Wu, Yada Zhu, Kommy Weldemariam, Hanghang Tong, Jingrui He. Neural Active Learning beyond Bandits. The 12th International Conference on Learning Representations (ICLR 2024).
• Rohan Deb, Yikun Ban, Shiliang Zuo, Jingrui He, Arindam Banerjee. Contextual Bandits with Online Neural Regression. The 12th International Conference on Learning Representations (ICLR 2024).
• Haonan Wang*, Ziwei Wu*, Jingrui He. Training Fair Deep Neural Networks by Balancing Influence. The 17th ACM International Conference on Web Search and Data Mining (WDSM 2024). (*Equal Contribution).
• Lecheng Zheng*, Dawei Zhou*, Hanghang Tong, Jiejun Xu, Yada Zhu, Jingrui He. FairGen: Towards Fair Graph Generation. IEEE International Conference on Data Engineering (ICDE 2024). (*Equal Contribution).

2023

• Wenxuan Bao*, Tianxin Wei*, Haohan Wang, Jingrui He. Adaptive Test-Time Personalization for Federated Learning. Thirty-Seventh Conference on Neural Information Processing Systems (NeurIPS' 2023), December 2023. (*Equal Contribution).
• Yunzhe Qi, Yikun Ban, Tianxin Wei, Jiaru Zou, Huaxiu Yao, Jingrui He. Meta-Learning with Neural Bandit Scheduler. Thirty-Seventh Conference on Neural Information Processing Systems (NeurIPS' 2023), December 2023.
• Jun Wu, Elizabeth Ainsworth, Andrew Leakey, Haixun Wang, Jingrui He. Graph-Structured Gaussian Processes for Transferable Graph Learning. Thirty-Seventh Conference on Neural Information Processing Systems (NeurIPS' 2023), December 2023.
• Dongqi Fu. Investigating Natural and Artificial Dynamics in Graph Data Mining and Machine Learning. The 32nd ACM International Conference on Information and Knowledge Management (CIKM' 2023), October 2023. (Doctoral Symposium)
• Xinrui He*, Tianxin Wei*, Jingrui He. Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning. The 32nd ACM International Conference on Information and Knowledge Management (CIKM' 2023), October 2023. (*Equal Contribution).
• Yunzhe Qi*, Yikun Ban*, and Jingrui He. Graph Neural Bandits. The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD' 2023), August 2023. (*Equal Contribution).
• Jun Wu, Wenxuan Bao, Elizabeth Ainsworth, Jingrui He. Personalized Federated Learning with Parameter Propagation. The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD' 2023), August 2023.
• Wenxuan Bao, Haohan Wang, Jun Wu, Jingrui He. Optimizing the Collaboration Structure in Cross-silo Federated Learning. The Fortieth International Conference on Machine Learning (ICML' 2023), July 2023.
• Tianxin Wei*, Zeming Guo*, Yifan Chen*, Jingrui He. NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning. The Fortieth International Conference on Machine Learning (ICML' 2023), July 2023. (*Equal Contribution).
• Dongqi Fu*, Wenxuan Bao*, Ross Maciejewski, Hanghang Tong, Jingrui He. Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey. SIGKDD Explorations, June 2023. (*Equal Contribution).
• Dongqi Fu, Dawei Zhou, Ross Maciejewski, Arie Croitoru, Marcus Boyd, and Jingrui He. Fairness-Aware Clique-Preserving Spectral Clustering of Temporal Graphs. The ACM Web Conference 2023 (WWW' 2023), April 2023.
• Zihao Li*, Dongqi Fu*, and Jingrui He. Everything Evolves in Personalized PageRank. The ACM Web Conference 2023 (WWW' 2023), April 2023. (*Equal Contribution).
• Lecheng Zheng, Yada Zhu, Jingrui He. Fairness-aware Multi-view Clustering. SIAM International Conference on Data Mining (SDM' 2023), April 2023.
• Dongqi Fu, Zhe Xu, Hanghang Tong, and Jingrui He. Natural and Artificial Dynamics in GNNs: A Tutorial, The 16th ACM International Conference on Web Search and Data Mining (WSDM' 2023), February 2023. (Tutorial)
• Jun Wu, Jingrui He, Elizabeth Ainsworth. Non-IID Transfer Learning on Graphs. Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI' 2023), February 2023.

2022

• Jun Wu, Jingrui He. A Unified Framework for Adversarial Attacks on Multi-Source Domain Adaptation. IEEE Transactions on Knowledge and Data Engineering (TKDE), December 2022.
• Dongqi Fu and Jingrui He. Natural and Artificial Dynamics in Graphs: Concept, Progress, and Future. Frontiers in Big Data, December 2022.
• Jun Wu, Hanghang Tong, Elizabeth Ainsworth, Jingrui He. Adaptive Knowledge Transfer on Evolving Domains. 2022 IEEE International Conference on Big Data (IEEE BigData' 2022), December 2022.
• Dongqi Fu, Jingrui He. DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data. 2022 IEEE International Conference on Big Data (IEEE BigData' 2022), December 2022.
• Jun Wu, Wenxuan Bao, Jingrui He. Learning from Non-IID Data: Centralized vs. Federated Learning. IEEE International Conference on Big Data (IEEE BigData' 2022), December 2022. (Tutorial)
• Jun Wu, Jingrui He. Dynamic Transfer Learning with Progressive Meta-task Scheduler. Frontiers in Big Data. November 2022
• Yikun Ban*, Yuheng Zhang*, Hanghang Tong, Arindam Banerjee, Jingrui He. Improved Algorithms for Neural Active Learning. The 36th Conference on Neural Information Processing Systems (NeurIPS' 2022), November 2022. (*Equal Contribution).
• Tianxin Wei*, Yuning You*, Tianlong Chen, Yang Shen, Jingrui He, Zhangyang Wang. Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative. The 36th Conference on Neural Information Processing Systems (NeurIPS' 2022), November 2022. (*Equal Contribution).
• Jun Wu, Jingrui He, Sheng Wang, Kaiyu Guan, Elizabeth Ainsworth. Distribution-Informed Neural Networks for Domain Adaptation Regression. The 36th Conference on Neural Information Processing Systems (NeurIPS' 2022), November 2022.
• Haonan Wang, Wei Huang, Ziwei Wu, Hanghang Tong, Andrew J Margenot, Jingrui He. Deep Active Learning by Leveraging Training Dynamics. The 36th Conference on Neural Information Processing Systems (NeurIPS' 2022), November 2022.
• Dawei Zhou*, Lecheng Zheng*, Dongqi Fu, Jiawei Han, and Jingrui He. MentorGNN: Deriving Curriculum for Pre-Training GNNs. The 31st ACM International Conference on Information and Knowledge Management (CIKM’ 2022), October 2022. (*Equal Contribution).
• Yao Zhou*, Jun Wu*, Haixun Wang, Jingrui He. Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning. The 31st ACM International Conference on Information and Knowledge Management (CIKM 2022), October 2022. (*Equal Contribution).
• Dongqi Fu, Yikun Ban, Hanghang Tong, Ross Maciejewski, and Jingrui He. DISCO: Comprehensive and Explainable Disinformation Detection, The 31st ACM International Conference on Information and Knowledge Management (CIKM 2022), October 2022.
• Yunzhe Qi, Yikun Ban, and Jingrui He. Neural Bandit with Arm Group Graph. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022), August 2022.
• Tianxin Wei, Jingrui He. Comprehensive Fair Meta-learned Recommender System. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022), August 2022.
• Jun Wu, Jingrui He. Domain Adaption with Dynamic Open-Set Targets. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022), August 2022.
• Lecheng Zheng, Jinjun Xiong, Yada Zhu, and Jingrui He. Contrastive Learning with complex heterogeneity. The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022), August 2022.
• Dongqi Fu*, Liri Fang*, Ross Maciejewski, Vetle I Torvik, and Jingrui He. Meta-Learned Metrics over Multi-Evolution Temporal Graphs, The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022), August 2022. (*Equal Contribution).
• Jun Wu, Jingrui He. A Unified Meta-Learning Framework for Dynamic Transfer Learning. International Joint Conference on Artificial Intelligence (IJCAI 2022), July 2022.
• Dongqi Fu, Jingrui He, Hanghang Tong, and Ross Maciejewski. Privacy-preserving Graph Analytics: Secure Generation and Federated Learning. Workshop on Privacy Enhancing Technologies for the Homeland Security Enterprise (PETS4HSE 2022), June 2022.
• Ziwei Wu, Jingrui He. Fairness-aware Model-agnostic Positive and Unlabeled Learning. ACM Conference on Fairness, Accountability, and Transparency (FAccT 2022), June 2022. Distinguished Paper Award.

2021

• Yikun Ban, Yuchen Yan, Arindam Banerjee, and Jingrui He. EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits (ICLR 2022), April 2022.
• Yikun Ban, Jingrui He, and Curtiss B. Cook. Multi-Facet Contextual Bandits: A Neural Network Perspective. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2021), August 2021. [Code]
• Jun Wu, Jingrui He. Indirect Invisible Poisoning Attacks on Domain Adaptation. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2021), August 2021. [Code]
• Yao Zhou, Jianpeng Xu, Jun Wu, Zeinab Taghavi Nasrabadi, Evren Korpeoglu, Kannan Achan, Jingrui He. PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2021), August 2021. [Code]
• Dongqi Fu and Jingrui He. SDG: A Simplified and Dynamic Graph Neural Network. The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2021), July 2021. [Code]
• Lecheng Zheng, Yu Cheng, Hongxia Yang, Nan Cao, and Jingrui He. Deep Co-Attention Network for Multi-View Subspace Learning. The Web Conference 2021 (WWW 2021), April 2021. [Code]
• Haonan Wang, Chang Zhou, Carl Yang, Hongxia Yang, and Jingrui He. Controllable Gradient Item Retrieval. The Web Conference 2021 (WWW 2021), April 2021. [Code]
• Yikun Ban and Jingrui He. Local Clustering in Contextual Multi-Armed Bandits. The Web Conference 2021 (WWW 2021), April 2021. [Code]
• Dawei Zhou, Si Zhang, Mehmet Yigit Yildirim, Scott Alcorn, Hanghang Tong, Hasan Davulcu, and Jingrui He. High-Order Structure Exploration on Massive Graphs: A Local Graph Clustering Perspective. ACM Transactions on Knowledge Discovery from Data (TKDD 2021), 2021.
• Jianbo Li, Lecheng Zheng, Yada Zhu, and Jingrui He. Outlier Impact Characterization for Time Series Data, The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021), February 2021.

2020

• Yang Shi, Yuyin Liu, Hanghang Tong, Jingrui He, Gang Yan, and Nan Cao. Visual Analytics of Anomalous User Behaviors: A Survey. IEEE Transactions on Big Data (2020).
• Pei Yang, Qi Tan, and Jingrui He. Complex Heterogeneity Learning: A Theoretical and Empirical Study. Pattern Recognition 107: 107519 (2020).
• Xu Liu, Jingrui He, Wanli Min, and Hongxia Yang. Missing Information Imputation for Disease-dedicated Social Networks with Heterogeneous Auxiliary Data. IISE Transactions on Healthcare Systems Engineering (2020).
• Jiacheng Pan, Dongming Han, Fangzhou Guo, Dawei Zhou, Nan Cao, Jingrui He, Mingliang Xu, and Wei Chen. RCAnalyzer: visual analytics of rare categories in dynamic networks. Frontiers Inf. Technol. Electron. Eng. 21(4): 491-506 (2020).
• Yuxin Ma, Arlen Fan, Jingrui He, Arun Reddy Nelakurthi, and Ross Maciejewski. A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes. VAST 2020.
• Dongqi Fu*, Zhe Xu*, Bo Li, Hanghang Tong, and Jingrui He. A View-Adversarial Framework for Multi-View Network Embedding, ACM International Conference on Information and Knowledge Management (CIKM 2020), October 2020. (*Equal Contribution). [Code]
• Dongqi Fu, Dawei Zhou, and Jingrui He. Local Motif Clustering on Time-Evolving Graphs, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020), August 2020. (Oral Presentation, AC rate = 17%). [Code]
• Yikun Ban and Jingrui He. Generic Outlier Detection in Multi-Armed Bandit, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020), August 2020. (Oral Presentation, AC rate = 17%). [Code]
• Dawei Zhou, Lecheng Zheng, Jiawei Han, and Jingrui He. A Data-Driven Graph Generative Model for Temporal Interaction Networks, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020), August 2020. (Oral Presentation, AC rate = 17%). [Code]
• Jian Kang, Jingrui He, Ross Maciejewski, and Hanghang Tong. InFoRM: Individual Fairness on Graph Mining, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2020), August 2020. (Oral Presentation, AC rate = 17%).
• Yao Zhou, Arun Reddy Nelakurthy, Ross Maciejewski, Wei Fan, and Jingrui He. Crowd Teaching with Imperfect Labels, The Web Conference 2020 (WWW 2020), April 2020. (Oral Presentation, AC rate = 19%). [Code]
• Dawei Zhou, Lecheng Zheng, Jianbo Li, Yada Zhu, and Jingrui He. Domain Adaptive Multi-Modality Neural Attention Network for Financial Forecasting, The Web Conference 2020 (WWW 2020), April 2020. (Oral Presentation, AC rate = 19%).
• Dawei Zhou*, Zhining Liu*, Yada Zhu, Jinjie Gu, and Jingrui He. Towards Fine-grained Temporal Network Representation via Time-Reinforced Random Walk, The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020), January 2020. (*Equal contribution). [Code]

2019

• Dawei Zhou*, Zhining Liu*, and Jingrui He. Towards Explainable Representation of Time-Evolving Graphs via Spatial-Temporal Graph Attention, ACM International Conference on Information and Knowledge Management (CIKM 2019) (*Equal contribution).
• Xu Liu, Jingrui He, Sam Duddy, and Liz O'Sullivan. Convolution-Consistent Collective Matrix Completion, ACM International Conference on Information and Knowledge Management (CIKM 2019).
• Jun Wu and Jingrui He. Scalable Manifold-Regularized Attributed Network Embedding via Maximum Mean Discrepancy, ACM International Conference on Information and Knowledge Management (CIKM-2019). [Code]
• Pei Yang, Qi Tan, Hanghang Tong, and Jingrui He. Task-Adversarial Co-Generative Nets, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019) (acceptance rate of research track: 14%).
• Jun Wu, Jingrui He, and Jiejun Xu. DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019) (acceptance rate of research track: 14%). [Code]
• Pei Yang, Qi Tan, Jieping Ye, Hanghang Tong, and Jingrui He. Deep Multi-Task Learning with Adversarial-and-Cooperative Nets, Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI 2019).
• Dawei Zhou, Lecheng Zheng, Jiejun Xu, and Jingrui He. Misc-GAN: A Multi-Scale Generative Model for Graphs, Frontier, March 2019.
• Lecheng Zheng, Yu Cheng, and Jingrui He. Deep Multimodality Model for Multi-task Multi-view Learning. (SDM 2019). [Code]
• Yao Zhou, Lei Ying, and Jingrui He. Multi-task Crowdsourcing via an Optimization Framework. ACM Transactions on Knowledge Discovery from Data (TKDD 2019).

2018

• Dawei Zhou, Jingrui He, Hasan Davulcu, and Ross Maciejewski. Motif-Preserving Dynamic Local Graph Cut (IEEE BigData-2018), December 2018.
• Jun Wu, Jingrui He, and Yongming Liu. ImVerde: Vertex-Diminished Random Walk for Learning Imbalanced Network Representation. (IEEE BigData 2018). [Code]
• Arun Reddy Nelakurthi, Ross Maciejewski, and Jingrui He. Source Free Domain Adaptation Using an Off-the-Shelf Classifier. (IEEE BigData 2018) [Code].
• Jianbo Li, Jingrui He, and Yada Zhu. E-tail Product Return Prediction via Hypergraph-based Local Graph Cut. (KDD-2018) (acceptance rate of applied data science track oral presentation: 8.1%). [Code]
• Yao Zhou, Arun Reddy Nelakurthi, and Jingrui He. Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2018) (acceptance rate of research track long presentation: 10.9%). [Code]
• Dawei Zhou, Jingrui He, Hongxia Yang, and Wei Fan. SPARC: Self-Paced Network Representation for Few-Shot Rare Category Characterization, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2018) (acceptance rate of research track short presentation: 18.4%). [Code]
• Yada Zhu, Jianbo Li, and Jingrui He, Brian L. Quanz, Ajay A. Deshpande. A Local Algorithm for Product Return Prediction in E-Commerce. (IJCAI 2018).
• Yao Zhou and Jingrui He. Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching. International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction (SBP 2018).
• Pengfei Jiang, Weina Wang, Yao Zhou, Jingrui He, and Lei Ying. A Winners-Take-All Incentive Mechanism for Crowd-Powered Systems. SIGMETRICS Workshop (NetEcon 2018).
• Pei Yang, Qi Tan, and Jingrui He. Function-on-Function Regression with Mode-Sparsity Regularization. ACM Transactions on Knowledge Discovery from Data (TKDD 2018).
• Hanfei Lin, Siyuan Gao, David Gotz, Fan Du, Jingrui He, and Nan Cao. RCLens: Interactive Rare Category Exploration and Identification. IEEE Transactions on Visualization and Computer Graphics (2018).
• Arun R Nelakurthi, Angela M Pinto, Curtiss B Cook, Lynne Jones, Mary Boyle, Jieping Ye, Theodoros Lappas, and Jingrui He. Should Patients with Diabetes Be Encouraged to Integrate Social Media into Their Care Plan? Future Science OA (2018).
• Pei Yang, Qi Tan, Yada Zhu, and Jingrui He. Heterogeneous Representation Learning with Separable Structured Sparsity Regularization. Knowledge and Information Systems (2018).
• Shuo Feng, Derong Shen, Tiezheng Nie, Yue Kou, Jingrui He, and Ge Yu. Inferring Anchor Links Based on Social Network Structure. IEEE Access 6: 17340-17353 (2018).
• Qi Tan, Pei Yang, and Jingrui He. Feature Co-Shrinking for Co-Clustering. Pattern Recognition (2018)

2017

• Dawei Zhou, Si Zhang, Mehmet Yigit Yildirim, Scott Alcorn, Hanghang Tong, Hasan Davulcu, and Jingrui He. A Local Algorithm for Structure-Preserving Graph Cut, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017) (acceptance rate of research track: 18.9%).
• Hongxia Yang, Yada Zhu, and Jingrui He. Local Algorithm for User Action Prediction Towards Display Ads. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017) (acceptance rate of applied data science track: 21.5%).
• Pei Yang, Qi Tan, and Jingrui He. Multi-task Function-on-function Regression with Co-grouping Structured Sparsity. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2017) (acceptance rate of research track: 18.9%).
• Arun Reddy Nelakurthi, and Jingrui He. Finding Cut from the Same Cloth. Cross Network Link Recommendation via Joint Matrix Factorization. (AAAI 2017).
• Arun Reddy Nelakurthi, Hanghang Tong, Ross Maciejewski, Nadya Bliss, and Jingrui He. User-guided Cross-domain Sentiment Classification. SIAM International Conference on Data Mining (SDM 2017).
• Yada Zhu, Jianbo Li, and Jingrui He. Learning from Multi-Modality Multi-Resolution Data: an Optimization Approach. SIAM International Conference on Data Mining (SDM 2017).
• Si Zhang, Dawei Zhou, Mehmet Yigit Yildirim, Scott Alcorn, Jingrui He, Hasan Davulcu, and Hanghang Tong. HiDDen: Hierarchical Dense Subgraph Detection with Application to Financial Fraud Detection. SIAM International Conference on Data Mining (SDM 2017).
• Yao Zhou, Lei Ying, and Jingrui He. MultiC2: an Optimization Framework for Learning from Task and Worker Dual Heterogeneity. SIAM International Conference on Data Mining (SDM 2017).
• Yao Zhou and Jingrui He. A Randomized Approach for Crowdsourcing in the Presence of Multiple Views. IEEE International Conference on Data Mining (ICDM 2017).
• Jianboi Li, Jingrui He, and Yada Zhu. HiMuV: Hierarchical Framework for Modeling Multi-Modality Multi-Resolution Data. IEEE International Conference on Data Mining (ICDM 2017).
• Jingrui He. Learning from Data Heterogeneity: Algorithms and Applications. (IJCAI 2017)
• Dawei Zhou, Arun Karthikeyan, Kangyang Wang, Nan Cao, and Jingrui He. Discovering Rare Categories from Graph Streams. Data Mining and Knowledge Discovery (2017)
• Chen Chen, Jingrui He, Nadya Bliss, and Hanghang Tong. Towards Optimal Connectivity on Multi-layered Networks. IEEE Transactions on Knowledge and Data Engineering (TKDE 2017).

2016

• Dawei Zhou, Jingrui He, Yu Cao, Jae-sun Seo. Bi-level Rare Temporal Pattern Detection, IEEE International Conference on Data Mining (ICDM 2016).
• Pei Yang and Jingrui He. Heterogeneous Representation Learning with Structured Sparsity Regularization. IEEE International Conference on Data Mining (ICDM 2016). [Invited to KAIS SI on “Bests of ICDM 2016”]
• Pei Yang and Jingrui He. Functional Regression with Mode-Sparsity Constraint. IEEE International Conference on Data Mining (ICDM 2016).
• Yao Zhou, Jingrui He. Crowdsourcing via Tensor Augmentation and Completion. Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-2016)
• Pei Yang, Hasan Davulcu, Yada Zhu, Jingrui He. A Generalized Hierarchical Multi-Latent Space Model for Heterogeneous Learning. IEEE Transactions on Knowledge and Data Engineering (TKDE 2016).
• Yada Zhu, Jingrui He. Co-clustering Structural Temporal Data with Applications to Semiconductor Manufacturing. ACM Transactions on Knowledge Discovery from Data (TKDD 2016).
• Pei Yang, Hongxia Yang, Haoda Fu, Dawei Zhou, Jiping Ye, Theodoros Lappas, Jingrui He. Joint Modeling Label and Feature Heterogeneity in Medical Informatics. ACM Transactions on Knowledge Discovery from Data (TKDD 2016).
• David Muchlinski, David Siroky, Jingrui He, and Matthew Kocher. Comparing Random Forest with Logistic Regression for Predicting Class-imbalanced Civil War Onset Data. Political Analysis 24(1): 87-103 (2016)
• Jingrui He. Discussion of “Reinforcement Learning Behaviors in Sponsored Search”. Applied Stochastic Models in Business and Industry 32(3): 368 (2016)

2015

• Pei Yang, Jingrui He, and Jia Yu Pan. Learning Complex Rare Categories with Dual Heterogeneity. (SDM 2015)
• David C. Kale, Marjan Ghazvininejad, Anil Ramakrishna, Jingrui He, and Yan Liu. Hierarchical Active Transfer Learning. (SDM 2015)
• Pei Yang and Jingrui He. A Graph-based Hybrid Framework for Modeling Complex Heterogeneity. (ICDM 2015)
• Dawei Zhou, Kangyang Wang, Nan Cao, and Jingrui He. Rare Category Detection on Time-Evolving Graphs, IEEE International Conference on Data Mining (ICDM 2015)
• Chen Chen, Jingrui He, Nadya Bliss, and Hanghang Tong. On the Connectivity of Multi-layered Networks: Models, Measures and Optimal Control. (ICDM 2015)
• Deqing Yang, Jingrui He, Huazheng Qin, Yanghua Xiao, and Wei Wang. A Graph-based Recommendation across Heterogeneous Domains. (CIKM 2015)
• Pei Yang and Jingrui He. Model Multiple Heterogeneity via Hierarchical Co-Latent Space Learning. (KDD 2015) (acceptance rate of research track: 19.4%)
• Yada Zhu, Hongxia Yang, and Jingrui He. Co-Clustering based Dual Prediction for Cargo Pricing Optimization. (KDD 2015) (acceptance rate of research track: 19.4%)
• Dawei Zhou, Jingrui He, K. Selcuk Candan, and Hasan Davulcu. MUVIR: Multi-View Rare Category Detection, The 24th International Joint Conference on Artificial Intelligence (IJCAI 2015)