Multiple types of heterogeneity naturally co-exist in a variety of high-impact real applications, such as manufacturing processes, abnormal user detection, etc. State-of-the-art data mining techniques are rich in handling a single type of heterogeneity, which are designed for a specific type of heterogeneity, and often inadequate in addressing the co-existence of multiple types of heterogeneity, referred to as Heterogeneity of Heterogeneity (HoH). To be specific, the following two questions largely remain unanswered: (Q1) how to jointly model multiple types of heterogeneity; (Q2) how to theoretically understand the model generalization performance in the context of HoH? The overall goal of this project is to model and understand the Heterogeneity of Heterogeneity in data mining, by (1) creating a suite of effective and efficient algorithms for modeling HoH, (2) characterizing the model generalization performance in the context of HoH , and (3) evaluate the proposed techniques on various real applications, such as abnormal user detection and quality control in manufacturing processes.
Wenxuan Bao, Jun Wu, and Jingrui He. BOBA: Byzantine-Robust Federated Learning with Label Skewness. AISTATS 2024
Haonan Wang, Ziwei Wu, Jingrui He. FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes. WSDM 2024
Dongqi Fu, Dawei Zhou, Ross Maciejewski, Arie Croitoru, Marcus Boyd, Jingrui He. Fairness-Aware Clique-Preserving Spectral Clustering of Temporal graphs. WWW 2023
Dongqi Fu, Wenxuan Bao, Ross Maciejewski, Hanghang Tong, Jingrui He. Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey. SIGKDD Explorations 2023
Jun Wu, Elizabeth Ainsworth, Andrew Leakey, Haixun Wang, Jingrui He. Graph-Structured Gaussian Processes for Transferable Graph Learning. NeurIPS 2023
Jun Wu, Jingrui He. A Unified Framework for Adversarial Attacks on Multi-Source Domain Adaptation. TKDE 2023
Jun Wu, Jingrui He, Elizabeth A. Ainsworth. Non-IID Transfer Learning on Graphs. AAAI 2023
Jun Wu, Wenxuan Bao, Elizabeth A. Ainsworth, Jingrui He. Personalized Federated Learning with Parameter Propagation. KDD 2023
Lecheng Zheng, Yada Zhu, Jingrui He. Fairness-aware Multi-view Clustering. SDM 2023
Tianxin Wei, Zeming Guo, Yifan Chen, Jingrui He. NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning. ICML 2023
Wenxuan Bao, Haohan Wang, Jun Wu, Jingrui He. Optimizing the Collaboration Structure in Cross-Silo Federated Learning. ICML 2023
Wenxuan Bao, Tianxin Wei, Haohan Wang, Jingrui He. Adaptive Test-Time Personalization for Federated Learning. NeurIPS 2023
Xinrui He, Tianxin Wei, Jingrui He. Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning. CIKM 2023
Yunzhe Qi, Yikun Ban, Jingrui He. Graph Neural Bandits. KDD 2023
Yunzhe Qi, Yikun Ban, Tianxin Wei, Jiaru Zou, Huaxiu Yao, Jingrui He. Meta-Learning with Neural Bandit Scheduler. NeurIPS 2023
Zihao Li, Dongqi Fu, Jingrui He. Everything Evolves in Personalized PageRank. WWW 2023
Tianxin Wei, Yuning You, Tianlong Chen, Yang Shen, Jingrui He, Zhangyang Wang. Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative. NeurIPS 2022
Haonan Wang, Wei Huang, Ziwei Wu, Hanghang Tong, Andrew J Margenot, Jingrui He. Deep Active Learning by Leveraging Training Dynamics. NeurIPS 2022
Yikun Ban, Yuheng Zhang, Hanghang Tong, Arindam Banerjee, Jingrui He. Improved Algorithms for Neural Active Learning. NeurIPS 2022
Jun Wu, Jingrui He, Sheng Wang, Kaiyu Guan, Elizabeth Ainsworth. Distribution-Informed Neural Networks for Domain Adaptation Regression. NeurIPS 2022
Dawei Zhou, Lecheng Zheng, Dongqi Fu, Jiawei Han, Jingrui He. MentorGNN: Deriving Curriculum for Pre-Training GNNs. CIKM 2022
Yao Zhou, Jun Wu, Haixun Wang, Jingrui He. Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning. CIKM 2022
Dongqi Fu, Yikun Ban, Hanghang Tong, Ross Maciejewski, Jingrui He. DISCO: Comprehensive and Explainable Disinformation Detection. CIKM 2022
Jun Wu, Jingrui He. Domain Adaptation with Dynamic Open-Set Targets. KDD 2022
Yunzhe Qi, Yikun Ban, Jingrui He. Neural Bandit with Arm Group Graph. KDD 2022
Tianxin Wei, Jingrui He. Comprehensive Fair Meta-learned Recommender System. KDD 2022
Lecheng Zheng, Jinjun Xiong, Yada Zhu, Jingrui He. Contrastive Learning with Complex Heterogeneity. KDD 2022
Dongqi Fu, Liri Fang, Ross Maciejewski, Vetle Tovik, Jingrui He. Meta-Learned Metrics over Multi-Evolution Temporal Graphs. KDD 2022
Jun Wu, Jingrui He. A Unified Meta-Learning Framework for Dynamic Transfer Learning. IJCAI 2022
Ziwei Wu, Jingrui He: Fairness-aware Model-agnostic Positive and Unlabeled Learning. FAccT 2022 [Distinguished Paper Award]
Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He. EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits. ICLR 2022
Jun Wu, Hanghang Tong, Elizabeth Ainsworth, Jingrui He. Adaptive Knowledge Transfer on Evolving Domains. IEEE BigData 2022
Dongqi Fu, Jingrui He. DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data. IEEE BigData 5th Special Session on HealthCare Data 2022
Yang Shi, Yuyin Liu, Hanghang Tong, Jingrui He, Gang Yan, Nan Cao, Visual Analytics of Anomalous User Behaviors: A Survey. IEEE Transactions on Big Data, 2022
Yikun Ban, Jingrui He, Curtiss B. Cook. Multi-facet Contextual Bandits: A Neural Network Perspective. KDD 2021
Yikun Ban, Jingrui He. Local Clustering in Contextual Multi-Armed Bandits. WWW 2021
Yao Zhou, Jianpeng Xu, Jun Wu, Zeinab Taghavi Nasrabadi, Evren Körpeoglu, Kannan Achan, Jingrui He. PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network. KDD 2021
Lecheng Zheng, Yu Cheng, Hongxia Yang, Nan Cao, Jingrui He. Deep Co-Attention Network for Multi-View Subspace Learning. WWW 2021
Jun Wu, Jingrui He. Indirect Invisible Poisoning Attacks on Domain Adaptation. KDD 2021
Jianbo Li, Lecheng Zheng, Yada Zhu, Jingrui He. Outlier Impact Characterization for Time Series Data. AAAI 2021
Haonan Wang, Chang Zhou, Carl Yang, Hongxia Yang, Jingrui He (2021). Controllable Gradient Item Retrieval. WWW 2021
Dongqi Fu, Jingrui He (2021). SDG: A Simplified and Dynamic Graph Neural Network. SIGIR 2021
Dawei Zhou, Si Zhang, Mehmet Yigit Yildirim, Scott Alcorn, Hanghang Tong, Hasan Davulcu, Jingrui He. High-Order Structure Exploration on Massive Graphs: A Local Graph Clustering Perspective. TKDD 2021
Xu Liu, Congzhe Su, Amey Barapatre, Xiaoting Zhao, Diane Hu, Chu-Cheng Hsieh, Jingrui He. Interpretable Attribute-based Action-aware Bandits for Within-Session Personalization in E-commerce. IEEE Data Engineering Bulletin 2021
Pei Yang, Qi Tan, Jingrui He. Complex Heterogeneity Learning: A Theoretical and Empirical Study. Pattern Recognition 2020
Dawei Zhou, Lecheng Zheng, Jiawei Han, Jingrui He. A Data Driven Graph Generative Model for Temporal Interaction Networks. KDD 2020
Jian Kang, Jingrui He, Ross Maciejewski, Hanghang Tong. InFoRM: Individual Fairness on Graph Mining. KDD 2020
Yikun Ban, Jingrui He. Generic Outlier Detection in Multi-Armed Bandit. KDD 2020
Dongqi Fu, Dawei Zhou, Jingrui He. Local Motif Clustering on Time-Evolving Graphs. KDD 2020
Yao Zhou, Arun Nelakurthi, Ross Maciejewski, Wei Fan, Jingrui He. Crowd Teaching with Imperfect Labels. WWW 2020
Dawei Zhou, Lecheng Zheng, Yada Zhu, Jianbo Li, Jingrui He. Domain Adaptive Multi-Modality Neural Attention Network for Financial Forecasting. WWW 2020
Zhining Liu, Dawei Zhou, Yada Zhu, Jinjie Gu, Jingrui He. Towards Fine-Grained Temporal Network Representation via Time-Reinforced Random Walk. AAAI 2020
Dongqi Fu, Zhe Xu, Bo Li, Hanghang Tong, Jingrui He. A View-Adversarial Framework for Multi-View Network Embedding. CIKM 2020
Arun Reddy Nelakurthi, Jingrui He. Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective. CRC Taylor and Francis Group, February 2020
Jinjun Xiong, Yada Zhu, and Jingrui He. Machine Learning for VLSI Chip Testing and Semiconductor Manufacturing Process Monitoring and Improvement. Machine Learning in VLSI Computer-Aided Design, ISBN 978-3-030-04666-8, pp. 233 – 263, 2019
Yao Zhou, Lei Ying, and J. He. Multi-task Crowdsourcing via an Optimization Framework. TKDD 2019
Jun Wu, Jingrui He, Jiejun Xu. DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification. KDD 2019: 406-415 code
Pei Yang, Qi Tan, Hanghang Tong, Jingrui He. Task-Adversarial Co-Generative Nets. KDD 2019: 1596-1604
Pei Yang, Qi Tan, Jieping Ye, Hanghang Tong, Jingrui He. Deep Multi-Task Learning with Adversarial-and-Cooperative Nets. IJCAI 2019: 4078-4084
Jun Wu, Jingrui He. Scalable Manifold-Regularized Attributed Network Embedding via Maximum Mean Discrepancy. CIKM 2019: 2101-2104 code
Zhining Liu, Dawei Zhou, Jingrui He. Towards Explainable Representation of Time-Evolving Graphs via Spatial-Temporal Graph Attention Networks. CIKM 2019: 2137-2140 code
Xu Liu, Jingrui He, Sam Duddy, Liz O'Sullivan. Convolution-Consistent Collective Matrix Completion. CIKM 2019: 2209-2212
Lecheng Zheng, Yu Cheng, Jingrui He. Deep Multimodality Model for Multi-task Multi-view Learning. SDM 2019: 10-18 code
Yao Zhou, Arun Reddy Nelakurthi, Jingrui He. Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners. KDD 2018: 2817-2826 code
Dawei Zhou, Jingrui He, Hongxia Yang, Wei Fan. SPARC: Self-Paced Network Representation for Few-Shot Rare Category Characterization. KDD 2018: 2807-2816 code
Jianbo Li, Jingrui He, Yada Zhu. E-tail Product Return Prediction via Hypergraph-based Local Graph Cut. KDD 2018: 519-527 code
Arun Reddy Nelakurthi, Ross Maciejewski, Jingrui He. Source Free Domain Adaptation Using an Off-the-Shelf Classifier. BigData 2018: 140-145 code
Jun Wu, Jingrui He, Yongming Liu. ImVerde: Vertex-Diminished Random Walk for Learning Imbalanced Network Representation. BigData 2018: 871-880 code
Dawei Zhou, Jingrui He, Hasan Davulcu, Ross Maciejewski. Motif-Preserving Dynamic Local Graph Cut. BigData 2018: 1156-1161
Yada Zhu, Jianbo Li, Jingrui He, Brian L. Quanz, Ajay A. Deshpande. A Local Algorithm for Product Return Prediction in E-Commerce. IJCAI 2018: 3718-3724
Lecheng Zheng, Baoyu Jing, Zihao Li, Hanghang Tong, Jingrui He. Heterogeneous Contrastive Learning for Foundation Models and Beyond, KDD 2024. link
Yikun Ban, Yunzhe Qi, Jingrui He. Neural Contextual Bandits for Personalized Recommendation, WWW 2024. link
Jun Wu, Jingrui He. Trustworthy Transfer Learning: Transferability and Trustworthiness, KDD 2023. link
Jun Wu, Wenxuan Bao, Jingrui He. Learning from Non-IID Data: Centralized vs. Federated Learning, IJCAI 2023. link
Dongqi Fu, Zhe Xu, Hanghang Tong, Jingrui He. Natural and Artificial Dynamics in GNNs, WSDM 2023. link
Lecheng Zheng, Jingrui He. Contrastive Learning: A Heterogeneous Perspective, SDM 2023. link
Jun Wu, Wenxuan Bao, Jingrui He. Learning from Non-IID Data: Centralized vs. Federated Learning. IEEE BigData 2022. link
Dawei Zhou, Jingrui He. Gold Panning from the Mess: Rare Category Exploration, Exposition, Representation, and Interpretation. KDD 2019. link
Yao Zhou, Fenglong Ma, Jing Gao, Jingrui He. Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching. KDD 2019. link