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ORCIDJingrui He
何京芮
Person information
- affiliation: University of Illinois at Urbana-Champaign, IL, USA
- affiliation: Arizona State University, AZ, USA
- affiliation (Ph.D., 2010): Carnegie Mellon University, PA, USA
- affiliation (former): Tsinghua University, China
- unicode name: 何京芮
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Books and Theses
- 2012
[b1]Jingrui He:
Analysis of Rare Categories. Cognitive Technologies, Springer 2012, ISBN 978-3-642-22812-4, pp. 1-128
Journal Articles
- 2024
- [j32]Michael C. Loui, Nigel Bosch, Anita Say Chan, Jenny L. Davis, Rochelle Gutiérrez, Jingrui He, Karrie Karahalios, Sanmi Koyejo, Ruby Mendenhall, Madelyn Rose Sanfilippo, Hanghang Tong, Lav R. Varshney, Yang Wang:
Artificial Intelligence, Social Responsibility, and the Roles of the University. Commun. ACM 67(8): 22-25 (2024) - [j31]Dawei Zhou, Jingrui He
:
Rare Category Analysis for Complex Data: A Review. ACM Comput. Surv. 56(5): 123:1-123:35 (2024) - 2023
- [j30]Dongqi Fu, Wenxuan Bao, Ross Maciejewski, Hanghang Tong, Jingrui He:
Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey. SIGKDD Explor. 25(1): 54-72 (2023) - [j29]Jun Wu, Jingrui He:
A Unified Framework for Adversarial Attacks on Multi-Source Domain Adaptation. IEEE Trans. Knowl. Data Eng. 35(11): 11039-11050 (2023) - 2022
- [j28]Dongming Han, Jiacheng Pan, Rusheng Pan, Dawei Zhou, Nan Cao, Jingrui He, Mingliang Xu, Wei Chen:
iNet: visual analysis of irregular transition in multivariate dynamic networks. Frontiers Comput. Sci. 16(2): 162701 (2022) - [j27]Dongqi Fu, Jingrui He:
Natural and Artificial Dynamics in Graphs: Concept, Progress, and Future. Frontiers Big Data 5 (2022) - [j26]Jun Wu, Jingrui He:
Dynamic transfer learning with progressive meta-task scheduler. Frontiers Big Data 5 (2022) - [j25]Yang Shi, Yuyin Liu, Hanghang Tong, Jingrui He, Gang Yan, Nan Cao:
Visual Analytics of Anomalous User Behaviors: A Survey. IEEE Trans. Big Data 8(2): 377-396 (2022) - 2021
- [j24]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 Eng. Bull. 44(2): 65-80 (2021) - [j23]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. ACM Trans. Knowl. Discov. Data 15(2): 18:1-18:26 (2021) - [j22]Yuxin Ma, Arlen Fan, Jingrui He, Arun Reddy Nelakurthi, Ross Maciejewski:
A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes. IEEE Trans. Vis. Comput. Graph. 27(2): 1385-1395 (2021) - 2020
- [j21]Jiacheng Pan, Dongming Han, Fangzhou Guo, Dawei Zhou, Nan Cao, Jingrui He, Mingliang Xu, Wei Chen:
RCAnalyzer: visual analytics of rare categories in dynamic networks. Frontiers Inf. Technol. Electron. Eng. 21(4): 491-506 (2020) - [j20]Pei Yang, Qi Tan, Jingrui He:
Complex heterogeneity learning: A theoretical and empirical study. Pattern Recognit. 107: 107519 (2020) - 2019
- [j19]Dawei Zhou, Lecheng Zheng, Jiejun Xu, Jingrui He:
Misc-GAN: A Multi-scale Generative Model for Graphs. Frontiers Big Data 2: 3 (2019) - [j18]Chieh-Yang Huang, Hanghang Tong, Jingrui He, Ross Maciejewski:
Location Prediction for Tweets. Frontiers Big Data 2: 5 (2019) - [j17]Yao Zhou, Lei Ying, Jingrui He:
Multi-task Crowdsourcing via an Optimization Framework. ACM Trans. Knowl. Discov. Data 13(3): 27:1-27:26 (2019) - 2018
- [j16]Shuo Feng, Derong Shen, Tiezheng Nie, Yue Kou, Jingrui He, Ge Yu:
Inferring Anchor Links Based on Social Network Structure. IEEE Access 6: 17340-17353 (2018) - [j15]Pei Yang, Qi Tan, Yada Zhu, Jingrui He:
Heterogeneous representation learning with separable structured sparsity regularization. Knowl. Inf. Syst. 55(3): 671-694 (2018) - [j14]Qi Tan, Pei Yang, Jingrui He:
Feature co-shrinking for co-clustering. Pattern Recognit. 77: 12-19 (2018) - [j13]Pei Yang, Qi Tan, Jingrui He:
Function-on-Function Regression with Mode-Sparsity Regularization. ACM Trans. Knowl. Discov. Data 12(3): 36:1-36:23 (2018) - [j12]Hanfei Lin, Siyuan Gao, David Gotz, Fan Du, Jingrui He, Nan Cao:
RCLens: Interactive Rare Category Exploration and Identification. IEEE Trans. Vis. Comput. Graph. 24(7): 2223-2237 (2018) - 2017
- [j11]Dawei Zhou, Arun Karthikeyan, Kangyang Wang, Nan Cao, Jingrui He:
Discovering rare categories from graph streams. Data Min. Knowl. Discov. 31(2): 400-423 (2017) - [j10]Chen Chen, Jingrui He, Nadya Bliss, Hanghang Tong:
Towards Optimal Connectivity on Multi-Layered Networks. IEEE Trans. Knowl. Data Eng. 29(10): 2332-2346 (2017) - 2016
- [j9]Pei Yang, Hongxia Yang, Haoda Fu, Dawei Zhou, Jieping Ye, Theodoros Lappas, Jingrui He:
Jointly Modeling Label and Feature Heterogeneity in Medical Informatics. ACM Trans. Knowl. Discov. Data 10(4): 39:1-39:25 (2016) - [j8]Yada Zhu, Jingrui He:
Co-Clustering Structural Temporal Data with Applications to Semiconductor Manufacturing. ACM Trans. Knowl. Discov. Data 10(4): 43:1-43:18 (2016) - [j7]Pei Yang, Hasan Davulcu, Yada Zhu, Jingrui He:
A Generalized Hierarchical Multi-Latent Space Model for Heterogeneous Learning. IEEE Trans. Knowl. Data Eng. 28(12): 3154-3168 (2016) - 2015
- [j6]Yada Zhu, Jingrui He, Richard D. Lawrence:
A general framework for predictive tensor modeling with domain knowledge. Data Min. Knowl. Discov. 29(6): 1709-1732 (2015) - 2012
- [j5]Jingrui He, Hanghang Tong, Jaime G. Carbonell:
An effective framework for characterizing rare categories. Frontiers Comput. Sci. 6(2): 154-165 (2012) - 2010
- [j4]Jingrui He, Jaime G. Carbonell:
Coselection of features and instances for unsupervised rare category analysis. Stat. Anal. Data Min. 3(6): 417-430 (2010) - 2007
- [j3]Fei Wu, Changshui Zhang, Jingrui He:
An evolutionary system for near-regular texture synthesis. Pattern Recognit. 40(8): 2271-2282 (2007) - 2006
- [j2]Hanghang Tong, Jingrui He, Mingjing Li, Wei-Ying Ma, Hong-Jiang Zhang, Changshui Zhang:
Manifold-Ranking-Based Keyword Propagation for Image Retrieval. EURASIP J. Adv. Signal Process. 2006 (2006) - [j1]Jingrui He, Mingjing Li, HongJiang Zhang, Hanghang Tong, Changshui Zhang:
Generalized Manifold-Ranking-Based Image Retrieval. IEEE Trans. Image Process. 15(10): 3170-3177 (2006)
Conference and Workshop Papers
- 2024
- [c150]Wenxuan Bao, Jun Wu, Jingrui He:
BOBA: Byzantine-Robust Federated Learning with Label Skewness. AISTATS 2024: 892-900 - [c149]Lecheng Zheng, Dawei Zhou, Hanghang Tong, Jiejun Xu, Yada Zhu, Jingrui He:
Fairgen: Towards Fair Graph Generation. ICDE 2024: 2285-2297 - [c148]Yikun Ban, Ishika Agarwal, Ziwei Wu, Yada Zhu, Kommy Weldemariam, Hanghang Tong, Jingrui He:
Neural Active Learning Beyond Bandits. ICLR 2024 - [c147]Rohan Deb, Yikun Ban, Shiliang Zuo, Jingrui He, Arindam Banerjee:
Contextual Bandits with Online Neural Regression. ICLR 2024 - [c146]Dongqi Fu, Zhigang Hua, Yan Xie, Jin Fang, Si Zhang, Kaan Sancak, Hao Wu, Andrey Malevich, Jingrui He, Bo Long:
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections. ICLR 2024 - [c145]Tianxin Wei, Bowen Jin, Ruirui Li, Hansi Zeng, Zhengyang Wang, Jianhui Sun, Qingyu Yin, Hanqing Lu, Suhang Wang, Jingrui He, Xianfeng Tang:
Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond. ICLR 2024 - [c144]Zhining Liu, Ruizhong Qiu, Zhichen Zeng, Hyunsik Yoo, David Zhou, Zhe Xu, Yada Zhu, Kommy Weldemariam, Jingrui He, Hanghang Tong:
Class-Imbalanced Graph Learning without Class Rebalancing. ICML 2024 - [c143]Zhichen Zeng, Ruizhong Qiu, Zhe Xu, Zhining Liu, Yuchen Yan, Tianxin Wei, Lei Ying, Jingrui He, Hanghang Tong:
Graph Mixup on Approximate Gromov-Wasserstein Geodesics. ICML 2024 - [c142]Yikun Ban, Yunzhe Qi, Tianxin Wei, Lihui Liu, Jingrui He:
Meta Clustering of Neural Bandits. KDD 2024: 95-106 - [c141]Jun Wu, Jingrui He, Hanghang Tong:
Distributional Network of Networks for Modeling Data Heterogeneity. KDD 2024: 3379-3390 - [c140]Lecheng Zheng, Baoyu Jing, Zihao Li, Hanghang Tong, Jingrui He:
Heterogeneous Contrastive Learning for Foundation Models and Beyond. KDD 2024: 6666-6676 - [c139]Zihao Li, Yuyi Ao, Jingrui He:
SpherE: Expressive and Interpretable Knowledge Graph Embedding for Set Retrieval. SIGIR 2024: 2629-2634 - [c138]Haonan Wang, Ziwei Wu, Jingrui He:
FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes. WSDM 2024: 721-730 - [c137]Yikun Ban, Yunzhe Qi, Jingrui He:
Neural Contextual Bandits for Personalized Recommendation. WWW (Companion Volume) 2024: 1246-1249 - [c136]Jingrui He, Jian Kang, Fatemeh Nargesian, Haohui Wang, An Zhang, Dawei Zhou:
TrustLOG: The Second Workshop on Trustworthy Learning on Graphs. WWW (Companion Volume) 2024: 1785-1788 - [c135]Xinrui He, Shuo Liu, Jacky Keung, Jingrui He:
Co-clustering for Federated Recommender System. WWW 2024: 3821-3832 - [c134]Lecheng Zheng, Zhengzhang Chen, Jingrui He, Haifeng Chen:
MULAN: Multi-modal Causal Structure Learning and Root Cause Analysis for Microservice Systems. WWW 2024: 4107-4116 - 2023
- [c133]Jun Wu, Jingrui He, Elizabeth A. Ainsworth:
Non-IID Transfer Learning on Graphs. AAAI 2023: 10342-10350 - [c132]Xinrui He, Tianxin Wei, Jingrui He:
Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning. CIKM 2023: 709-719 - [c131]Wenxuan Bao, Haohan Wang, Jun Wu, Jingrui He:
Optimizing the Collaboration Structure in Cross-Silo Federated Learning. ICML 2023: 1718-1736 - [c130]Tianxin Wei, Zeming Guo, Yifan Chen, Jingrui He:
NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning. ICML 2023: 36821-36838 - [c129]Yunzhe Qi, Yikun Ban, Jingrui He:
Graph Neural Bandits. KDD 2023: 1920-1931 - [c128]Jun Wu, Wenxuan Bao, Elizabeth A. Ainsworth, Jingrui He:
Personalized Federated Learning with Parameter Propagation. KDD 2023: 2594-2605 - [c127]Jun Wu, Jingrui He:
Trustworthy Transfer Learning: Transferability and Trustworthiness. KDD 2023: 5829-5830 - [c126]Jiaqi Ma, Jiong Zhu, Yuxiao Dong, Danai Koutra, Jingrui He, Qiaozhu Mei, Anton Tsitsulin, Xingjian Zhang, Marinka Zitnik:
The 3rd Workshop on Graph Learning Benchmarks (GLB 2023). KDD 2023: 5870-5871 - [c125]Wenxuan Bao, Tianxin Wei, Haohan Wang, Jingrui He:
Adaptive Test-Time Personalization for Federated Learning. NeurIPS 2023 - [c124]Yunzhe Qi, Yikun Ban, Tianxin Wei, Jiaru Zou, Huaxiu Yao, Jingrui He:
Meta-Learning with Neural Bandit Scheduler. NeurIPS 2023 - [c123]Jun Wu, Lisa Ainsworth, Andrew Leakey, Haixun Wang, Jingrui He:
Graph-Structured Gaussian Processes for Transferable Graph Learning. NeurIPS 2023 - [c122]Lecheng Zheng, Yada Zhu, Jingrui He:
Fairness-aware Multi-view Clustering. SDM 2023: 856-864 - [c121]Dongqi Fu, Zhe Xu, Hanghang Tong, Jingrui He:
Natural and Artificial Dynamics in GNNs: A Tutorial. WSDM 2023: 1252-1255 - [c120]Zihao Li, Dongqi Fu, Jingrui He:
Everything Evolves in Personalized PageRank. WWW 2023: 3342-3352 - [c119]Dongqi Fu, Dawei Zhou, Ross Maciejewski, Arie Croitoru, Marcus Boyd, Jingrui He:
Fairness-Aware Clique-Preserving Spectral Clustering of Temporal Graphs. WWW 2023: 3755-3765 - 2022
- [c118]Jun Wu, Hanghang Tong, Elizabeth A. Ainsworth, Jingrui He:
Adaptive Knowledge Transfer on Evolving Domains. IEEE Big Data 2022: 1389-1394 - [c117]Dongqi Fu, Jingrui He:
DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data. IEEE Big Data 2022: 5269-5277 - [c116]Dawei Zhou, Lecheng Zheng, Dongqi Fu, Jiawei Han, Jingrui He:
MentorGNN: Deriving Curriculum for Pre-Training GNNs. CIKM 2022: 2721-2731 - [c115]Yao Zhou, Jun Wu, Haixun Wang, Jingrui He:
Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning. CIKM 2022: 2753-2762 - [c114]Dongqi Fu, Yikun Ban, Hanghang Tong, Ross Maciejewski, Jingrui He:
DISCO: Comprehensive and Explainable Disinformation Detection. CIKM 2022: 4848-4852 - [c113]Jian Kang, Shuaicheng Zhang, Bo Li, Jingrui He, Jian Pei, Dawei Zhou:
TrustLOG: The First Workshop on Trustworthy Learning on Graphs. CIKM 2022: 5169-5170 - [c112]Ziwei Wu, Jingrui He:
Fairness-aware Model-agnostic Positive and Unlabeled Learning. FAccT 2022: 1698-1708 - [c111]Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He:
EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits. ICLR 2022 - [c110]Jun Wu, Jingrui He:
A Unified Meta-Learning Framework for Dynamic Transfer Learning. IJCAI 2022: 3573-3579 - [c109]Dongqi Fu, Liri Fang, Ross Maciejewski, Vetle I. Torvik, Jingrui He:
Meta-Learned Metrics over Multi-Evolution Temporal Graphs. KDD 2022: 367-377 - [c108]Yunzhe Qi, Yikun Ban, Jingrui He:
Neural Bandit with Arm Group Graph. KDD 2022: 1379-1389 - [c107]Tianxin Wei, Jingrui He:
Comprehensive Fair Meta-learned Recommender System. KDD 2022: 1989-1999 - [c106]Jun Wu, Jingrui He:
Domain Adaptation with Dynamic Open-Set Targets. KDD 2022: 2039-2049 - [c105]Lecheng Zheng, Jinjun Xiong, Yada Zhu, Jingrui He:
Contrastive Learning with Complex Heterogeneity. KDD 2022: 2594-2604 - [c104]Jun Wu, Jingrui He, Sheng Wang, Kaiyu Guan, Elizabeth A. Ainsworth:
Distribution-Informed Neural Networks for Domain Adaptation Regression. NeurIPS 2022 - [c103]Yikun Ban, Yuheng Zhang, Hanghang Tong, Arindam Banerjee, Jingrui He:
Improved Algorithms for Neural Active Learning. NeurIPS 2022 - [c102]Haonan Wang, Wei Huang, Ziwei Wu, Hanghang Tong, Andrew Margenot, Jingrui He:
Deep Active Learning by Leveraging Training Dynamics. NeurIPS 2022 - [c101]Tianxin Wei, Yuning You, Tianlong Chen, Yang Shen, Jingrui He, Zhangyang Wang:
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative. NeurIPS 2022 - 2021
- [c100]Jianbo Li, Lecheng Zheng, Yada Zhu, Jingrui He:
Outlier Impact Characterization for Time Series Data. AAAI 2021: 11595-11603 - [c99]Yikun Ban, Jingrui He, Curtiss B. Cook:
Multi-facet Contextual Bandits: A Neural Network Perspective. KDD 2021: 35-45 - [c98]Jun Wu, Jingrui He:
Indirect Invisible Poisoning Attacks on Domain Adaptation. KDD 2021: 1852-1862 - [c97]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: 2409-2419 - [c96]Dongqi Fu, Jingrui He:
SDG: A Simplified and Dynamic Graph Neural Network. SIGIR 2021: 2273-2277 - [c95]Haonan Wang, Chang Zhou, Carl Yang, Hongxia Yang, Jingrui He:
Controllable Gradient Item Retrieval. WWW 2021: 768-777 - [c94]Lecheng Zheng, Yu Cheng, Hongxia Yang, Nan Cao, Jingrui He:
Deep Co-Attention Network for Multi-View Subspace Learning. WWW 2021: 1528-1539 - [c93]Yikun Ban, Jingrui He:
Local Clustering in Contextual Multi-Armed Bandits. WWW 2021: 2335-2346 - 2020
- [c92]Zhining Liu, Dawei Zhou, Yada Zhu, Jinjie Gu, Jingrui He:
Towards Fine-Grained Temporal Network Representation via Time-Reinforced Random Walk. AAAI 2020: 4973-4980 - [c91]Shane Roach, Connie Ni, Alexei Kopylov, Tsai-Ching Lu, Jiejun Xu, Si Zhang, Boxin Du, Dawei Zhou, Jun Wu, Lihui Liu, Yuchen Yan, Jingrui He, Hanghang Tong:
CANON: Complex Analytics of Network of Networks for Modeling Adversarial Activities. IEEE BigData 2020: 1634-1643 - [c90]Dongqi Fu, Zhe Xu, Bo Li, Hanghang Tong, Jingrui He:
A View-Adversarial Framework for Multi-View Network Embedding. CIKM 2020: 2025-2028 - [c89]Jian Kang, Jingrui He, Ross Maciejewski, Hanghang Tong:
InFoRM: Individual Fairness on Graph Mining. KDD 2020: 379-389 - [c88]Dongqi Fu, Dawei Zhou, Jingrui He:
Local Motif Clustering on Time-Evolving Graphs. KDD 2020: 390-400 - [c87]Dawei Zhou, Lecheng Zheng, Jiawei Han, Jingrui He:
A Data-Driven Graph Generative Model for Temporal Interaction Networks. KDD 2020: 401-411 - [c86]Yikun Ban, Jingrui He:
Generic Outlier Detection in Multi-Armed Bandit. KDD 2020: 913-923 - [c85]Yao Zhou, Arun Reddy Nelakurthi, Ross Maciejewski, Wei Fan, Jingrui He:
Crowd Teaching with Imperfect Labels. WWW 2020: 110-121 - [c84]Dawei Zhou, Lecheng Zheng, Yada Zhu, Jianbo Li, Jingrui He:
Domain Adaptive Multi-Modality Neural Attention Network for Financial Forecasting. WWW 2020: 2230-2240 - 2019
- [c83]Jun Wu, Jingrui He:
Scalable Manifold-Regularized Attributed Network Embedding via Maximum Mean Discrepancy. CIKM 2019: 2101-2104 - [c82]Zhining Liu, Dawei Zhou, Jingrui He:
Towards Explainable Representation of Time-Evolving Graphs via Spatial-Temporal Graph Attention Networks. CIKM 2019: 2137-2140 - [c81]Xu Liu, Jingrui He, Sam Duddy, Liz O'Sullivan:
Convolution-Consistent Collective Matrix Completion. CIKM 2019: 2209-2212 - [c80]Pei Yang, Qi Tan, Jieping Ye, Hanghang Tong, Jingrui He:
Deep Multi-Task Learning with Adversarial-and-Cooperative Nets. IJCAI 2019: 4078-4084 - [c79]Jun Wu, Jingrui He, Jiejun Xu:
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification. KDD 2019: 406-415 - [c78]Pei Yang, Qi Tan, Hanghang Tong, Jingrui He:
Task-Adversarial Co-Generative Nets. KDD 2019: 1596-1604 - [c77]Dawei Zhou, Jingrui He:
Gold Panning from the Mess: Rare Category Exploration, Exposition, Representation, and Interpretation. KDD 2019: 3213-3214 - [c76]Yao Zhou, Fenglong Ma, Jing Gao, Jingrui He:
Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching. KDD 2019: 3231-3232 - [c75]Lecheng Zheng, Yu Cheng, Jingrui He:
Deep Multimodality Model for Multi-task Multi-view Learning. SDM 2019: 10-18 - 2018
- [c74]Arun Reddy Nelakurthi, Ross Maciejewski, Jingrui He:
Source Free Domain Adaptation Using an Off-the-Shelf Classifier. IEEE BigData 2018: 140-145 - [c73]Jun Wu, Jingrui He, Yongming Liu:
ImVerde: Vertex-Diminished Random Walk for Learning Imbalanced Network Representation. IEEE BigData 2018: 871-880 - [c72]Dawei Zhou, Jingrui He, Hasan Davulcu, Ross Maciejewski:
Motif-Preserving Dynamic Local Graph Cut. IEEE BigData 2018: 1156-1161 - [c71]Yada Zhu, Jianbo Li, Jingrui He, Brian Leo Quanz, Ajay A. Deshpande:
A Local Algorithm for Product Return Prediction in E-Commerce. IJCAI 2018: 3718-3724 - [c70]Jianbo Li, Jingrui He, Yada Zhu:
E-tail Product Return Prediction via Hypergraph-based Local Graph Cut. KDD 2018: 519-527 - [c69]Dawei Zhou, Jingrui He, Hongxia Yang, Wei Fan:
SPARC: Self-Paced Network Representation for Few-Shot Rare Category Characterization. KDD 2018: 2807-2816 - [c68]Yao Zhou, Arun Reddy Nelakurthi, Jingrui He:
Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners. KDD 2018: 2817-2826 - [c67]Pengfei Jiang, Weina Wang, Yao Zhou, Jingrui He, Lei Ying:
A Winners-Take-All Incentive Mechanism for Crowd-Powered Systems. NetEcon@SIGMETRICS 2018: 3:1-3:6 - [c66]Jiejun Xu, Hanghang Tong, Tsai-Ching Lu, Jingrui He, Nadya Bliss:
GTA3 2018: Workshop on Graph Techniques for Adversarial Activity Analytics. WSDM 2018: 803 - 2017
- [c65]Arun Reddy Nelakurthi, Jingrui He:
Finding Cut from the Same Cloth: Cross Network Link Recommendation via Joint Matrix Factorization. AAAI 2017: 1467-1473 - [c64]Jianbo Li, Jingrui He, Yada Zhu:
HiMuV: Hierarchical Framework for Modeling Multi-modality Multi-resolution Data. ICDM 2017: 267-276 - [c63]Yao Zhou, Jingrui He:
A Randomized Approach for Crowdsourcing in the Presence of Multiple Views. ICDM 2017: 685-694 - [c62]Jingrui He:
Learning from Data Heterogeneity: Algorithms and Applications. IJCAI 2017: 5126-5130 - [c61]Dawei Zhou, Si Zhang, Mehmet Yigit Yildirim, Scott Alcorn, Hanghang Tong, Hasan Davulcu, Jingrui He:
A Local Algorithm for Structure-Preserving Graph Cut. KDD 2017: 655-664 - [c60]Pei Yang, Qi Tan, Jingrui He:
Multi-task Function-on-function Regression with Co-grouping Structured Sparsity. KDD 2017: 1255-1264 - [c59]Hongxia Yang, Yada Zhu, Jingrui He:
Local Algorithm for User Action Prediction Towards Display Ads. KDD 2017: 2091-2099 - [c58]Arun Reddy Nelakurthi, Hanghang Tong, Ross Maciejewski, Nadya Bliss, Jingrui He:
User-guided Cross-domain Sentiment Classification. SDM 2017: 471-479 - [c57]Si Zhang, Dawei Zhou, Mehmet Yigit Yildirim, Scott Alcorn, Jingrui He, Hasan Davulcu, Hanghang Tong:
HiDDen: Hierarchical Dense Subgraph Detection with Application to Financial Fraud Detection. SDM 2017: 570-578 - [c56]Yao Zhou, Lei Ying, Jingrui He:
MultiC2: an Optimization Framework for Learning from Task and Worker Dual Heterogeneity. SDM 2017: 579-587 - [c55]Yada Zhu, Jianbo Li, Jingrui He:
Learning from Multi-Modality Multi-Resolution Data: an Optimization Approach. SDM 2017: 714-722 - 2016
- [c54]Pei Yang, Jingrui He:
Heterogeneous Representation Learning with Structured Sparsity Regularization. ICDM 2016: 539-548 - [c53]Dawei Zhou, Jingrui He, Yu Cao, Jae-sun Seo:
Bi-Level Rare Temporal Pattern Detection. ICDM 2016: 719-728 - [c52]Pei Yang, Jingrui He:
Functional Regression with Mode-Sparsity Constraint. ICDM 2016: 1311-1316 - [c51]Yao Zhou, Jingrui He:
Crowdsourcing via Tensor Augmentation and Completion. IJCAI 2016: 2435-2441 - 2015
- [c50]Deqing Yang, Jingrui He, Huazheng Qin, Yanghua Xiao, Wei Wang:
A Graph-based Recommendation across Heterogeneous Domains. CIKM 2015: 463-472 - [c49]Chen Chen, Jingrui He, Nadya Bliss, Hanghang Tong:
On the Connectivity of Multi-layered Networks: Models, Measures and Optimal Control. ICDM 2015: 715-720 - [c48]Pei Yang, Jingrui He:
A Graph-Based Hybrid Framework for Modeling Complex Heterogeneity. ICDM 2015: 1081-1086 - [c47]Dawei Zhou, Kangyang Wang, Nan Cao, Jingrui He:
Rare Category Detection on Time-Evolving Graphs. ICDM 2015: 1135-1140 - [c46]Chenhao Xie, Deqing Yang, Jingrui He, Yanghua Xiao:
Cross-Site Virtual Social Network Construction. ICDM Workshops 2015: 1660-1663 - [c45]Dawei Zhou, Jingrui He, K. Selçuk Candan, Hasan Davulcu:
MUVIR: Multi-View Rare Category Detection. IJCAI 2015: 4098-4104 - [c44]Pei Yang, Jingrui He:
Model Multiple Heterogeneity via Hierarchical Multi-Latent Space Learning. KDD 2015: 1375-1384 - [c43]Yada Zhu, Hongxia Yang, Jingrui He:
Co-Clustering based Dual Prediction for Cargo Pricing Optimization. KDD 2015: 1583-1592 - [c42]David C. Kale, Marjan Ghazvininejad, Anil Ramakrishna, Jingrui He, Yan Liu:
Hierarchical Active Transfer Learning. SDM 2015: 514-522 - [c41]Pei Yang, Jingrui He, Jia-Yu Pan:
Learning Complex Rare Categories with Dual Heterogeneity. SDM 2015: 523-531 - 2014
- [c40]Pei Yang, Jingrui He, Hongxia Yang, Haoda Fu:
Learning from Label and Feature Heterogeneity. ICDM 2014: 1079-1084 - [c39]Yada Zhu, Jingrui He:
Co-Clustering Structural Temporal Data with Applications to Semiconductor Manufacturing. ICDM 2014: 1121-1126 - [c38]Hongxia Yang, Jingrui He:
Learning with dual heterogeneity: a nonparametric bayes model. KDD 2014: 582-590 - [c37]Jingrui He, Yan Liu, Qiang Yang:
Linking Heterogeneous Input Spaces with Pivots for Multi-Task Learning. SDM 2014: 181-189 - 2013
- [c36]Dan Zhang, Jingrui He, Luo Si, Richard D. Lawrence:
MILEAGE: Multiple Instance LEArning with Global Embedding. ICML (3) 2013: 82-90 - [c35]Jingrui He, Wei Shen, Phani Divakaruni, Laura Wynter, Rick Lawrence:
Improving Traffic Prediction with Tweet Semantics. IJCAI 2013: 1387-1393 - [c34]Dan Zhang, Jingrui He, Richard D. Lawrence:
MI2LS: multi-instance learning from multiple informationsources. KDD 2013: 149-157 - 2012
- [c33]Yada Zhu, Jingrui He, Rick Lawrence:
Hierarchical Modeling with Tensor Inputs. AAAI 2012: 1233-1239 - [c32]Jingrui He, Yada Zhu:
Hierarchical Multi-task Learning with Application to Wafer Quality Prediction. ICDM 2012: 290-298 - [c31]Jingrui He, Hanghang Tong, Qiaozhu Mei, Boleslaw K. Szymanski:
GenDeR: A Generic Diversified Ranking Algorithm. NIPS 2012: 1151-1159 - [c30]Xi Chen, Jingrui He, Rick Lawrence, Jaime G. Carbonell:
Adaptive Multi-task Sparse Learning with an Application to fMRI Study. SDM 2012: 212-223 - 2011
- [c29]Jingrui He, Rick Lawrence:
A Graphbased Framework for Multi-Task Multi-View Learning. ICML 2011: 25-32 - [c28]Hanghang Tong, Jingrui He, Zhen Wen, Ravi B. Konuru, Ching-Yung Lin:
Diversified ranking on large graphs: an optimization viewpoint. KDD 2011: 1028-1036 - [c27]Dan Zhang, Jingrui He, Yan Liu, Luo Si, Richard D. Lawrence:
Multi-view transfer learning with a large margin approach. KDD 2011: 1208-1216 - 2010
- [c26]Jingrui He, Hanghang Tong, Jaime G. Carbonell:
Rare Category Characterization. ICDM 2010: 226-235 - [c25]Jingrui He, Qing He, Grzegorz Swirszcz, Yiannis Kamarianakis, Rick Lawrence, Wei Shen, Laura Wynter:
Ensemble-Based Method for Task 2: Predicting Traffic Jam. ICDM Workshops 2010: 1363-1365 - [c24]Wei Shen, Yiannis Kamarianakis, Laura Wynter, Jingrui He, Qing He, Rick Lawrence, Grzegorz Swirszcz:
Traffic Velocity Prediction Using GPS Data: IEEE ICDM Contest Task 3 Report. ICDM Workshops 2010: 1369-1371 - [c23]Jingrui He, Jaime G. Carbonell:
Co-selection of Features and Instances for Unsupervised Rare Category Analysis. SDM 2010: 525-536 - 2009
- [c22]Jingrui He, Yan Liu, Richard D. Lawrence:
Graph-based transfer learning. CIKM 2009: 937-946 - [c21]Jingrui He, Jaime G. Carbonell:
Prior-Free Rare Category Detection. SDM 2009: 155-163 - 2008
- [c20]Jingrui He, Yan Liu, Richard D. Lawrence:
Graph-Based Rare Category Detection. ICDM 2008: 833-838 - [c19]Jingrui He, Jaime G. Carbonell:
Rare Class Discovery Based on Active Learning. ISAIM 2008 - 2007
- [c18]Jingrui He, Bo Thiesson:
Asymmetric Gradient Boosting with Application to Spam Filtering. CEAS 2007 - [c17]Jingrui He, Jaime G. Carbonell, Yan Liu:
Graph-Based Semi-Supervised Learning as a Generative Model. IJCAI 2007: 2492-2497 - [c16]Jingrui He, Jaime G. Carbonell:
Nearest-Neighbor-Based Active Learning for Rare Category Detection. NIPS 2007: 633-640 - 2005
- [c15]Hanghang Tong, Jingrui He, Mingjing Li, Wei-Ying Ma, Changshui Zhang, HongJiang Zhang:
A Unified Optimization Based Learning Method for Image Retrieval. CVPR (2) 2005: 230-235 - [c14]Jingrui He, Changshui Zhang, Nanyuan Zhao, Hanghang Tong:
Boosting Web Image Search by Co-Ranking. ICASSP (2) 2005: 409-412 - [c13]Hanghang Tong, Chongrong Li, Jingrui He, Yang Chen:
Internet Traffic Prediction by W-Boost: Classification and Regression. ISNN (3) 2005: 397-402 - [c12]Hanghang Tong, Chongrong Li, Jingrui He, Jiajian Chen, Quang-Anh Tran, Hai-Xin Duan, Xing Li:
Anomaly Internet Network Traffic Detection by Kernel Principle Component Classifier. ISNN (3) 2005: 476-481 - [c11]Jingrui He, Hanghang Tong, Mingjing Li, Wei-Ying Ma, Changshui Zhang:
Multiple random walk and its application in content-based image retrieval. Multimedia Information Retrieval 2005: 151-158 - [c10]Hanghang Tong, Jingrui He, Mingjing Li, Changshui Zhang, Wei-Ying Ma:
Graph based multi-modality learning. ACM Multimedia 2005: 862-871 - [c9]Hanghang Tong, Mingjing Li, HongJiang Zhang, Changshui Zhang, Jingrui He, Wei-Ying Ma:
Learning No-Reference Quality Metric by Examples. MMM 2005: 247-254 - 2004
- [c8]Jingrui He, Mingjing Li, HongJiang Zhang, Changshui Zhang:
Symmetry feature in content-based image retrieval. ICIP 2004: 417-420 - [c7]Jingrui He, Mingjing Li, HongJiang Zhang, Changshui Zhang:
W-Boost and Its Application to Web Image Classification. ICPR (1) 2004: 148-151 - [c6]Hanghang Tong, Chongrong Li, Jingrui He:
A Boosting-Based Framework for Self-Similar and Non-linear Internet Traffic Prediction. ISNN (2) 2004: 931-936 - [c5]Jingrui He, Hanghang Tong, Mingjing Li, HongJiang Zhang, Changshui Zhang:
Mean version space: a new active learning method for content-based image retrieval. Multimedia Information Retrieval 2004: 15-22 - [c4]Jingrui He, Mingjing Li, HongJiang Zhang, Hanghang Tong, Changshui Zhang:
Manifold-ranking based image retrieval. ACM Multimedia 2004: 9-16 - [c3]Jingrui He, Mingjing Li, HongJiang Zhang, Hanghang Tong, Changshui Zhang:
Automatic Peak Number Detection in Image Symmetry Analysis. PCM (3) 2004: 111-118 - [c2]Hanghang Tong, Mingjing Li, HongJiang Zhang, Jingrui He, Changshui Zhang:
Classification of Digital Photos Taken by Photographers or Home Users. PCM (1) 2004: 198-205 - [c1]Jingrui He, Mingjing Li, Zhiwei Li, HongJiang Zhang, Hanghang Tong, Changshui Zhang:
Pseudo Relevance Feedback Based on Iterative Probabilistic One-Class SVMs in Web Image Retrieval. PCM (2) 2004: 213-220
Editorship
- 2023
- [e3]Jingrui He, Themis Palpanas, Xiaohua Hu, Alfredo Cuzzocrea, Dejing Dou, Dominik Slezak, Wei Wang, Aleksandra Gruca, Jerry Chun-Wei Lin, Rakesh Agrawal:
IEEE International Conference on Big Data, BigData 2023, Sorrento, Italy, December 15-18, 2023. IEEE 2023, ISBN 979-8-3503-2445-7 [contents] - 2018
- [e2]Naoki Abe, Huan Liu, Calton Pu, Xiaohua Hu, Nesreen K. Ahmed, Mu Qiao, Yang Song, Donald Kossmann, Bing Liu, Kisung Lee, Jiliang Tang, Jingrui He, Jeffrey S. Saltz:
IEEE International Conference on Big Data (IEEE BigData 2018), Seattle, WA, USA, December 10-13, 2018. IEEE 2018, ISBN 978-1-5386-5035-6 [contents] - 2013
- [e1]Inderjit S. Dhillon, Yehuda Koren, Rayid Ghani, Ted E. Senator, Paul Bradley, Rajesh Parekh, Jingrui He, Robert L. Grossman, Ramasamy Uthurusamy:
The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, IL, USA, August 11-14, 2013. ACM 2013, ISBN 978-1-4503-2174-7 [contents]
Reference Works
- 2018
- [r2]Yada Zhu, Jingrui He:
Social Phishing. Encyclopedia of Social Network Analysis and Mining. 2nd Ed. 2018 - 2014
- [r1]Jingrui He, Yada Zhu:
Social Engineering/Phishing. Encyclopedia of Social Network Analysis and Mining 2014: 1777-1783
Informal and Other Publications
- 2024
- [i60]Lecheng Zheng, Zhengzhang Chen, Jingrui He, Haifeng Chen:
Multi-modal Causal Structure Learning and Root Cause Analysis. CoRR abs/2402.02357 (2024) - [i59]Tianxin Wei, Bowen Jin, Ruirui Li, Hansi Zeng, Zhengyang Wang, Jianhui Sun, Qingyu Yin, Hanqing Lu, Suhang Wang, Jingrui He, Xianfeng Tang:
Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond. CoRR abs/2403.10667 (2024) - [i58]Baoyu Jing, Yansen Wang, Guoxin Sui, Jing Hong, Jingrui He, Yuqing Yang, Dongsheng Li, Kan Ren:
Automated Contrastive Learning Strategy Search for Time Series. CoRR abs/2403.12641 (2024) - [i57]Dongqi Fu, Zhigang Hua, Yan Xie, Jin Fang, Si Zhang, Kaan Sancak, Hao Wu, Andrey Malevich, Jingrui He, Bo Long:
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections. CoRR abs/2403.16030 (2024) - [i56]Lecheng Zheng, Baoyu Jing, Zihao Li, Hanghang Tong, Jingrui He:
Heterogeneous Contrastive Learning for Foundation Models and Beyond. CoRR abs/2404.00225 (2024) - [i55]Lihui Liu, Zihao Wang, Ruizhong Qiu, Yikun Ban, Eunice Chan, Yangqiu Song, Jingrui He, Hanghang Tong:
Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphs. CoRR abs/2404.04264 (2024) - [i54]Yikun Ban, Ishika Agarwal, Ziwei Wu, Yada Zhu, Kommy Weldemariam, Hanghang Tong, Jingrui He:
Neural Active Learning Beyond Bandits. CoRR abs/2404.12522 (2024) - [i53]Zihao Li, Yuyi Ao, Jingrui He:
SpherE: Expressive and Interpretable Knowledge Graph Embedding for Set Retrieval. CoRR abs/2404.19130 (2024) - [i52]Dongqi Fu, Yada Zhu, Hanghang Tong, Kommy Weldemariam, Onkar Bhardwaj, Jingrui He:
Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection. CoRR abs/2408.04254 (2024) - [i51]Yikun Ban, Yunzhe Qi, Tianxin Wei, Lihui Liu, Jingrui He:
Meta Clustering of Neural Bandits. CoRR abs/2408.05586 (2024) - 2023
- [i50]Lecheng Zheng, Yada Zhu, Jingrui He:
Fairness-aware Multi-view Clustering. CoRR abs/2302.05788 (2023) - [i49]Lecheng Zheng, Dawei Zhou, Hanghang Tong, Jiejun Xu, Yada Zhu, Jingrui He:
FairGen: Towards Fair Graph Generation. CoRR abs/2303.17743 (2023) - [i48]Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He:
Neural Exploitation and Exploration of Contextual Bandits. CoRR abs/2305.03784 (2023) - [i47]Wenxuan Bao, Haohan Wang, Jun Wu, Jingrui He:
Optimizing the Collaboration Structure in Cross-Silo Federated Learning. CoRR abs/2306.06508 (2023) - [i46]Dongqi Fu, Wenxuan Bao, Ross Maciejewski, Hanghang Tong, Jingrui He:
Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey. CoRR abs/2307.04338 (2023) - [i45]Tianxin Wei, Zeming Guo, Yifan Chen, Jingrui He:
NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning. CoRR abs/2307.08941 (2023) - [i44]Yunzhe Qi, Yikun Ban, Jingrui He:
Graph Neural Bandits. CoRR abs/2308.10808 (2023) - [i43]Zhining Liu, Zhichen Zeng, Ruizhong Qiu, Hyunsik Yoo, David Zhou, Zhe Xu, Yada Zhu, Kommy Weldemariam, Jingrui He, Hanghang Tong:
Topological Augmentation for Class-Imbalanced Node Classification. CoRR abs/2308.14181 (2023) - [i42]Wenxuan Bao, Tianxin Wei, Haohan Wang, Jingrui He:
Adaptive Test-Time Personalization for Federated Learning. CoRR abs/2310.18816 (2023) - [i41]Xinrui He, Tianxin Wei, Jingrui He:
Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning. CoRR abs/2311.16334 (2023) - [i40]Haoyang Liu, Tiancheng Xing, Luwei Li, Vibhu Dalal, Jingrui He, Haohan Wang:
Dataset Distillation via the Wasserstein Metric. CoRR abs/2311.18531 (2023) - [i39]Rohan Deb, Yikun Ban, Shiliang Zuo, Jingrui He, Arindam Banerjee:
Contextual Bandits with Online Neural Regression. CoRR abs/2312.07145 (2023) - [i38]Yikun Ban, Yunzhe Qi, Jingrui He:
Neural Contextual Bandits for Personalized Recommendation. CoRR abs/2312.14037 (2023) - 2022
- [i37]Jun Wu, Elizabeth A. Ainsworth, Sheng Wang, Kaiyu Guan, Jingrui He:
Adaptive Transfer Learning for Plant Phenotyping. CoRR abs/2201.05261 (2022) - [i36]Haonan Wang, Ziwei Wu, Jingrui He:
Training Fair Deep Neural Networks by Balancing Influence. CoRR abs/2201.05759 (2022) - [i35]Yikun Ban, Yunzhe Qi, Tianxin Wei, Jingrui He:
Neural Collaborative Filtering Bandits via Meta Learning. CoRR abs/2201.13395 (2022) - [i34]Dongqi Fu, Yikun Ban, Hanghang Tong, Ross Maciejewski, Jingrui He:
DISCO: Comprehensive and Explainable Disinformation Detection. CoRR abs/2203.04928 (2022) - [i33]Yunzhe Qi, Yikun Ban, Jingrui He:
Neural Bandit with Arm Group Graph. CoRR abs/2206.03644 (2022) - [i32]Tianxin Wei, Jingrui He:
Comprehensive Fair Meta-learned Recommender System. CoRR abs/2206.04789 (2022) - [i31]Ziwei Wu, Jingrui He:
Fairness-aware Model-agnostic Positive and Unlabeled Learning. CoRR abs/2206.09346 (2022) - [i30]Dongqi Fu, Jingrui He, Hanghang Tong, Ross Maciejewski:
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning. CoRR abs/2207.00048 (2022) - [i29]Jun Wu, Jingrui He:
A Unified Meta-Learning Framework for Dynamic Transfer Learning. CoRR abs/2207.01784 (2022) - [i28]Dawei Zhou, Lecheng Zheng, Dongqi Fu, Jiawei Han, Jingrui He:
MentorGNN: Deriving Curriculum for Pre-Training GNNs. CoRR abs/2208.09905 (2022) - [i27]Wenxuan Bao, Jingrui He:
BOBA: Byzantine-Robust Federated Learning with Label Skewness. CoRR abs/2208.12932 (2022) - [i26]Yikun Ban, Yuheng Zhang, Hanghang Tong, Arindam Banerjee, Jingrui He:
Improved Algorithms for Neural Active Learning. CoRR abs/2210.00423 (2022) - [i25]Tianxin Wei, Yuning You, Tianlong Chen, Yang Shen, Jingrui He, Zhangyang Wang:
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative. CoRR abs/2210.03801 (2022) - [i24]Jun Wu, Jingrui He, Elizabeth A. Ainsworth:
Non-IID Transfer Learning on Graphs. CoRR abs/2212.08174 (2022) - 2021
- [i23]Lecheng Zheng, Yu Cheng, Hongxia Yang, Nan Cao, Jingrui He:
Deep Co-Attention Network for Multi-View Subspace Learning. CoRR abs/2102.07751 (2021) - [i22]Yikun Ban, Jingrui He:
Local Clustering in Contextual Multi-Armed Bandits. CoRR abs/2103.00063 (2021) - [i21]Lecheng Zheng, Yada Zhu, Jingrui He, Jinjun Xiong:
Heterogeneous Contrastive Learning. CoRR abs/2105.09401 (2021) - [i20]Haonan Wang, Chang Zhou, Carl Yang, Hongxia Yang, Jingrui He:
Controllable Gradient Item Retrieval. CoRR abs/2106.00062 (2021) - [i19]Yikun Ban, Jingrui He, Curtiss B. Cook:
Multi-facet Contextual Bandits: A Neural Network Perspective. CoRR abs/2106.03039 (2021) - [i18]Dongqi Fu, Jingrui He:
DPPIN: A Biological Dataset of Dynamic Protein-Protein Interaction Networks. CoRR abs/2107.02168 (2021) - [i17]Yikun Ban, Jingrui He:
Convolutional Neural Bandit: Provable Algorithm for Visual-aware Advertising. CoRR abs/2107.07438 (2021) - [i16]Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He:
EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits. CoRR abs/2110.03177 (2021) - [i15]Haonan Wang, Wei Huang, Andrew Margenot, Hanghang Tong, Jingrui He:
Deep Active Learning by Leveraging Training Dynamics. CoRR abs/2110.08611 (2021) - [i14]Lecheng Zheng, Dongqi Fu, Jingrui He:
Tackling Oversmoothing of GNNs with Contrastive Learning. CoRR abs/2110.13798 (2021) - [i13]Yao Zhou, Haonan Wang, Jingrui He, Haixun Wang:
From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems. CoRR abs/2110.14844 (2021) - 2020
- [i12]Jun Wu, Jingrui He:
Continuous Transfer Learning with Label-informed Distribution Alignment. CoRR abs/2006.03230 (2020) - [i11]Yikun Ban, Jingrui He:
Generic Outlier Detection in Multi-Armed Bandit. CoRR abs/2007.07293 (2020) - [i10]Yuxin Ma, Arlen Fan, Jingrui He, Arun Reddy Nelakurthi, Ross Maciejewski:
A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes. CoRR abs/2009.06876 (2020) - [i9]Yao Zhou, Jun Wu, Jingrui He:
Robust Decentralized Learning for Neural Networks. CoRR abs/2009.09026 (2020) - [i8]Yao Zhou, Jianpeng Xu, Jun Wu, Zeinab Taghavi Nasrabadi, Evren Körpeoglu, Kannan Achan, Jingrui He:
GAN-based Recommendation with Positive-Unlabeled Sampling. CoRR abs/2012.06901 (2020) - 2019
- [i7]Lecheng Zheng, Yu Cheng, Jingrui He:
Deep Multimodality Model for Multi-task Multi-view Learning. CoRR abs/1901.08723 (2019) - [i6]Yang Shi, Yuyin Liu, Hanghang Tong, Jingrui He, Gang Yan, Nan Cao:
Visual Analytics of Anomalous User Behaviors: A Survey. CoRR abs/1905.06720 (2019) - [i5]Jun Wu, Jingrui He, Jiejun Xu:
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification. CoRR abs/1906.02319 (2019) - [i4]Yikun Ban, Yuchen Zhou, Jingrui He, Xu Cheng, Jiangfang Yi:
Coalesced TLB to Exploit Diverse Contiguity of Memory Mapping. CoRR abs/1908.08774 (2019) - 2018
- [i3]Yao Zhou, Arun Reddy Nelakurthi, Jingrui He:
Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners. CoRR abs/1804.06481 (2018) - [i2]Jun Wu, Jingrui He, Yongming Liu:
ImVerde: Vertex-Diminished Random Walk for Learning Network Representation from Imbalanced Data. CoRR abs/1804.09222 (2018) - [i1]Yao Zhou, Jingrui He:
Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching. CoRR abs/1806.09018 (2018)
Coauthor Index
[j32] [j30] [j25] [j23] [j18] [j10] [j5] [j2] [j1] [c149] [c148] [c144] [c143] [c141] [c140] [c121] [c118] [c114] [c103] [c102] [c91] [c90] [c89] [c80] [c78] [c66] [c61] [c58] [c57] [c49] [c31] [c28] [c26] [c15] [c14] [c13] [c12] [c11] [c10] [c9] [c6] [c5] [c4] [c3] [c2] [c1] [i56] [i55] [i54] [i52] [i49] [i46] [i43] [i34] [i30] [i26] [i15] [i6]
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