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ORCIDJie Lu 0001
Person information
- affiliation: University of Technology Sydney, Faculty of Engineering and Information Technology, Australia
- affiliation (PhD 2000): Curtin University, Bentley, WA, Australia
Other persons with the same name
- Jie Lu (aka: Lu Jie) — disambiguation page
- Jie Lu 0002 — Carnegie Mellon University, Pittsburgh, PA, USA
- Jie Lu 0003
![[0000-0002-6318-4666]](https://dblp1.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-6318-4666")
— Beihang University, State Key Laboratory of Virtual Reality Technology and Systems, Beijing, China - Jie Lu 0004 — JD.com, China (and 1 more)
- Jie Lu 0005 — KTH Royal Institute of Technology, School of Electrical Engineering, ACCESS Linnaeus Center, Sweden
- Jie Lu 0006 (aka: Lu Jie 0006) — National Digital Switching System Engineering and Technological Research and Development Center, Zhengzhou, China (and 2 more)
- Jie Lu 0007 — ShanghaiTech University, School of Information Science and Technology, China (and 1 more)
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2020 – today
- 2023
[j229]Suyue Han, Bin Liu, Xinyue Fan, Tingting Feng, JingJing Yang, Zhongli Zhou, Hao Gong, Jie Lu:
A new approach for landslide susceptibility assessments based on KDE-MDBN: A case study from mountainous regions impacted by the Wenchuan earthquake, China. Environ. Model. Softw. 167: 105759 (2023)- [j228]Haotian Xu, Zheng Yan, Junyu Xuan, Guangquan Zhang, Jie Lu:
Improving proximal policy optimization with alpha divergence. Neurocomputing 534: 94-105 (2023) - [j227]Yi Zhang, Mengjia Wu, Guangquan Zhang, Jie Lu:
Stepping beyond your comfort zone: Diffusion-based network analytics for knowledge trajectory recommendation. J. Assoc. Inf. Sci. Technol. 74(7): 775-790 (2023) - [j226]Zhen Fang, Jie Lu, Feng Liu, Guangquan Zhang:
Semi-Supervised Heterogeneous Domain Adaptation: Theory and Algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 45(1): 1087-1105 (2023) - [j225]Hang Yu, Weixu Liu, Jie Lu, Yimin Wen, Xiangfeng Luo, Guangquan Zhang:
Detecting group concept drift from multiple data streams. Pattern Recognit. 134: 109113 (2023) - [j224]Kun Wang, Jie Lu, Anjin Liu, Guangquan Zhang, Li Xiong:
Evolving Gradient Boost: A Pruning Scheme Based on Loss Improvement Ratio for Learning Under Concept Drift. IEEE Trans. Cybern. 53(4): 2110-2123 (2023) - [j223]Fujin Zhu, Jie Lu, Adi Lin, Junyu Xuan, Guangquan Zhang:
Direct Learning With Multi-Task Neural Networks for Treatment Effect Estimation. IEEE Trans. Knowl. Data Eng. 35(3): 2457-2470 (2023) - [j222]Qian Zhang, Wenhui Liao, Guangquan Zhang, Bo Yuan, Jie Lu:
A Deep Dual Adversarial Network for Cross-Domain Recommendation. IEEE Trans. Knowl. Data Eng. 35(4): 3266-3278 (2023) - [j221]Anjin Liu, Jie Lu, Yiliao Song, Junyu Xuan, Guangquan Zhang:
Concept Drift Detection Delay Index. IEEE Trans. Knowl. Data Eng. 35(5): 4585-4597 (2023) - [j220]Keqiuyin Li, Jie Lu, Hua Zuo, Guangquan Zhang:
Dynamic Classifier Alignment for Unsupervised Multi-Source Domain Adaptation. IEEE Trans. Knowl. Data Eng. 35(5): 4727-4740 (2023) - [j219]Qian Liu, Xiubo Geng, Heyan Huang, Tao Qin, Jie Lu, Daxin Jiang:
MGRC: An End-to-End Multigranularity Reading Comprehension Model for Question Answering. IEEE Trans. Neural Networks Learn. Syst. 34(5): 2594-2605 (2023) - [j218]Li Zhong, Zhen Fang, Feng Liu, Bo Yuan, Guangquan Zhang, Jie Lu:
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation. IEEE Trans. Neural Networks Learn. Syst. 34(8): 3859-3873 (2023) - [j217]Yiliao Song, Jie Lu, Haiyan Lu, Guangquan Zhang:
Learning Data Streams With Changing Distributions and Temporal Dependency. IEEE Trans. Neural Networks Learn. Syst. 34(8): 3952-3965 (2023) - [c204]Guohang Zeng, Zhen Fang, Guangquan Zhang, Jie Lu:
One-step Domain Adaptation Approach with Partial Label. IJCNN 2023: 1-8 - 2022
- [j216]Bin Wang, Jie Lu, Tianrui Li, Zheng Yan, Guangquan Zhang:
A quantile fusion methodology for deep forecasting. Neurocomputing 483: 286-298 (2022) - [j215]Kun Wang, Jie Lu, Anjin Liu, Yiliao Song, Li Xiong, Guangquan Zhang:
Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation. Neurocomputing 491: 288-304 (2022) - [j214]En Yu, Yiliao Song, Guangquan Zhang, Jie Lu:
Learn-to-adapt: Concept drift adaptation for hybrid multiple streams. Neurocomputing 496: 121-130 (2022) - [j213]Kai Yang, Jie Lu, Wanggen Wan, Guangquan Zhang, Li Hou:
Transfer learning based on sparse Gaussian process for regression. Inf. Sci. 605: 286-300 (2022) - [j212]Hang Yu, Qingyong Zhang, Tianyu Liu, Jie Lu, Yimin Wen, Guangquan Zhang:
Meta-ADD: A meta-learning based pre-trained model for concept drift active detection. Inf. Sci. 608: 996-1009 (2022) - [j211]Di Zhang, Zhongli Zhou, Suyue Han, Hao Gong, Tianyi Zou, Jie Lu:
Deep Metallogenic prediction model construction of the Xiongcun no. II orebody based on the DNN algorithm. Multim. Tools Appl. 81(23): 33185-33203 (2022) - [j210]Guanjin Wang, Kup-Sze Choi, Jeremy Yuen-Chun Teoh, Jie Lu:
Deep Cross-Output Knowledge Transfer Using Stacked-Structure Least-Squares Support Vector Machines. IEEE Trans. Cybern. 52(5): 3207-3220 (2022) - [j209]Hang Yu, Jie Lu, Guangquan Zhang:
Continuous Support Vector Regression for Nonstationary Streaming Data. IEEE Trans. Cybern. 52(5): 3592-3605 (2022) - [j208]Guanjin Wang, Ta Zhou, Kup-Sze Choi, Jie Lu:
A Deep-Ensemble-Level-Based Interpretable Takagi-Sugeno-Kang Fuzzy Classifier for Imbalanced Data. IEEE Trans. Cybern. 52(5): 3805-3818 (2022) - [j207]Fan Dong, Jie Lu, Yiliao Song, Feng Liu, Guangquan Zhang:
A Drift Region-Based Data Sample Filtering Method. IEEE Trans. Cybern. 52(9): 9377-9390 (2022) - [j206]Hang Yu, Jie Lu, Guangquan Zhang:
Topology Learning-Based Fuzzy Random Neural Networks for Streaming Data Regression. IEEE Trans. Fuzzy Syst. 30(2): 412-425 (2022) - [j205]Xiaoya Che, Hua Zuo, Jie Lu, Degang Chen:
Fuzzy Multioutput Transfer Learning for Regression. IEEE Trans. Fuzzy Syst. 30(7): 2438-2451 (2022) - [j204]Hang Yu, Jie Lu, Anjin Liu, Bin Wang, Ruimin Li, Guangquan Zhang:
Real-Time Prediction System of Train Carriage Load Based on Multi-Stream Fuzzy Learning. IEEE Trans. Intell. Transp. Syst. 23(9): 15155-15165 (2022) - [j203]Hang Yu, Jie Lu, Guangquan Zhang:
An Online Robust Support Vector Regression for Data Streams. IEEE Trans. Knowl. Data Eng. 34(1): 150-163 (2022) - [j202]Yiliao Song, Jie Lu, Anjin Liu, Haiyan Lu, Guangquan Zhang:
A Segment-Based Drift Adaptation Method for Data Streams. IEEE Trans. Neural Networks Learn. Syst. 33(9): 4876-4889 (2022) - [j201]Keqiuyin Li, Jie Lu, Hua Zuo, Guangquan Zhang:
Multi-Source Contribution Learning for Domain Adaptation. IEEE Trans. Neural Networks Learn. Syst. 33(10): 5293-5307 (2022) - [j200]Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu:
Learning From a Complementary-Label Source Domain: Theory and Algorithms. IEEE Trans. Neural Networks Learn. Syst. 33(12): 7667-7681 (2022) - [j199]Hang Yu, Jie Lu, Guangquan Zhang:
MORStreaming: A Multioutput Regression System for Streaming Data. IEEE Trans. Syst. Man Cybern. Syst. 52(8): 4862-4874 (2022) - [c203]Wei Duan, Junyu Xuan, Maoying Qiao, Jie Lu:
Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples. AAAI 2022: 6550-6558 - [c202]Zhaoqing Liu, Anjin Liu, Guangquan Zhang, Jie Lu:
An Empirical Study of Fuzzy Decision Tree for Gradient Boosting Ensemble. AI 2022: 716-727 - [c201]Jie Lu:
Machine Learning for Decision Support in Complex Environments. COMPLEXIS 2022: 9 - [c200]Keqiuyin Li, Jie Lu, Hua Zuo, Guangquan Zhang:
Source-Free Multi-Domain Adaptation with Generally Auxiliary Model Training. IJCNN 2022: 1-8 - [c199]Jie Lu:
Machine Learning for Decision Support in Complex Environments. IoTBDS 2022: 5 - [c198]Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu:
Is Out-of-Distribution Detection Learnable? NeurIPS 2022 - [i33]Yi Zhang, Mengjia Wu, Jie Lu:
Stepping beyond your comfort zone: Diffusion-based network analytics for knowledge trajectory recommendation. CoRR abs/2205.15504 (2022) - [i32]Guangzhi Ma, Jie Lu, Feng Liu, Zhen Fang, Guangquan Zhang:
Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms. CoRR abs/2206.04311 (2022) - [i31]Wei Duan, Junyu Xuan, Maoying Qiao, Jie Lu:
Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples. CoRR abs/2210.00728 (2022) - [i30]Tianyu Liu, Jie Lu, Zheng Yan, Guangquan Zhang:
Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision making. CoRR abs/2210.08486 (2022) - [i29]Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong, Bo Han, Feng Liu:
Is Out-of-Distribution Detection Learnable? CoRR abs/2210.14707 (2022) - [i28]Wei Duan, Junyu Xuan, Maoying Qiao, Jie Lu:
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes. CoRR abs/2212.02055 (2022) - 2021
- [j198]Wenjun Chang, Qian Zhang, Chao Fu, Weiyong Liu, Guangquan Zhang, Jie Lu:
A cross-domain recommender system through information transfer for medical diagnosis. Decis. Support Syst. 143: 113489 (2021) - [j197]Mengjia Wu, Yi Zhang, Mark Grosser, Steven Tipper, Deon Venter, Hua Lin, Jie Lu:
Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics. Frontiers Res. Metrics Anal. 6: 683212 (2021) - [j196]Nur Atiqah Rochin Demong, Jie Lu, Farookh Khadeer Hussain:
An Adaptive Personalized Property Investment Risk Analysis Method Based on Data-Driven Approach. Int. J. Inf. Technol. Decis. Mak. 20(2): 671-706 (2021) - [j195]Tianyu Liu, Jie Lu, Zheng Yan, Guangquan Zhang:
Statistical generalization performance guarantee for meta-learning with data dependent prior. Neurocomputing 465: 391-405 (2021) - [j194]Qian Liu, Jie Lu, Guangquan Zhang, Tao Shen, Zhihan Zhang, Heyan Huang:
Domain-specific meta-embedding with latent semantic structures. Inf. Sci. 555: 410-423 (2021) - [j193]Kai Yang, Jie Lu, Wanggen Wan, Guangquan Zhang:
Multi-source transfer regression via source-target pairwise segment. Inf. Sci. 556: 389-403 (2021) - [j192]Yi Zhang, Mengjia Wu, Wen Miao, Lu Huang, Jie Lu:
Bi-layer network analytics: A methodology for characterizing emerging general-purpose technologies. J. Informetrics 15(4): 101202 (2021) - [j191]Yi Zhang, Mengjia Wu, George Yijun Tian, Guangquan Zhang, Jie Lu:
Ethics and privacy of artificial intelligence: Understandings from bibliometrics. Knowl. Based Syst. 222: 106994 (2021) - [j190]Anjin Liu, Jie Lu, Guangquan Zhang:
Concept Drift Detection via Equal Intensity k-Means Space Partitioning. IEEE Trans. Cybern. 51(6): 3198-3211 (2021) - [j189]Qian Liu, Heyan Huang, Junyu Xuan, Guangquan Zhang, Yang Gao, Jie Lu:
A Fuzzy Word Similarity Measure for Selecting Top-$k$ Similar Words in Query Expansion. IEEE Trans. Fuzzy Syst. 29(8): 2132-2144 (2021) - [j188]Anjin Liu, Jie Lu, Guangquan Zhang:
Concept Drift Detection: Dealing With Missing Values via Fuzzy Distance Estimations. IEEE Trans. Fuzzy Syst. 29(11): 3219-3233 (2021) - [j187]Feng Liu, Guangquan Zhang, Jie Lu:
Multisource Heterogeneous Unsupervised Domain Adaptation via Fuzzy Relation Neural Networks. IEEE Trans. Fuzzy Syst. 29(11): 3308-3322 (2021) - [j186]Junyu Xuan, Jie Lu, Guangquan Zhang:
Bayesian Nonparametric Unsupervised Concept Drift Detection for Data Stream Mining. ACM Trans. Intell. Syst. Technol. 12(1): 5:1-5:22 (2021) - [j185]Mohammad Siami, Mohsen Naderpour, Jie Lu:
A Mobile Telematics Pattern Recognition Framework for Driving Behavior Extraction. IEEE Trans. Intell. Transp. Syst. 22(3): 1459-1472 (2021) - [j184]Anjin Liu, Jie Lu, Guangquan Zhang:
Diverse Instance-Weighting Ensemble Based on Region Drift Disagreement for Concept Drift Adaptation. IEEE Trans. Neural Networks Learn. Syst. 32(1): 293-307 (2021) - [j183]Zhen Fang, Jie Lu, Feng Liu, Junyu Xuan, Guangquan Zhang:
Open Set Domain Adaptation: Theoretical Bound and Algorithm. IEEE Trans. Neural Networks Learn. Syst. 32(10): 4309-4322 (2021) - [j182]Guanjin Wang, Kok Wai Wong, Jie Lu:
AUC-Based Extreme Learning Machines for Supervised and Semi-Supervised Imbalanced Classification. IEEE Trans. Syst. Man Cybern. Syst. 51(12): 7919-7930 (2021) - [c197]Li Zhong, Zhen Fang, Feng Liu, Jie Lu, Bo Yuan, Guangquan Zhang:
How Does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches? AAAI 2021: 11079-11087 - [c196]Keqiuyin Li, Jie Lu, Hua Zuo, Guangquan Zhang:
Multi-Source Domain Adaptation with Fuzzy-Rule based Deep Neural Networks. FUZZ-IEEE 2021: 1-6 - [c195]Guangzhi Ma, Feng Liu, Guangquan Zhang, Jie Lu:
Learning from Imprecise Observations: An Estimation Error Bound based on Fuzzy Random Variables. FUZZ-IEEE 2021: 1-8 - [c194]Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang:
Learning Bounds for Open-Set Learning. ICML 2021: 3122-3132 - [c193]Ming Zhou, Yiliao Song, Guangquan Zhang, Bin Zhang, Jie Lu:
An Efficient Bayesian Neural Network for Multiple Data Streams. IJCNN 2021: 1-8 - [c192]Wei Duan, Junyu Xuan, Jie Lu:
Negative Samples-enhanced Graph Convolutional Neural Networks. ISKE 2021: 262-268 - [c191]Feng Liu, Wenkai Xu, Jie Lu, Danica J. Sutherland:
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data. NeurIPS 2021: 5848-5860 - [i27]Li Zhong, Zhen Fang, Feng Liu, Jie Lu, Bo Yuan, Guangquan Zhang:
How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches? CoRR abs/2101.01104 (2021) - [i26]Tianyu Liu, Jie Lu, Zheng Yan, Guangquan Zhang:
PAC-Bayes Bounds for Meta-learning with Data-Dependent Prior. CoRR abs/2102.03748 (2021) - [i25]Hang Yu, Tianyu Liu, Jie Lu, Guangquan Zhang:
Automatic Learning to Detect Concept Drift. CoRR abs/2105.01419 (2021) - [i24]Feng Liu, Wenkai Xu, Jie Lu, Danica J. Sutherland:
Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data. CoRR abs/2106.07636 (2021) - [i23]Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang:
Learning Bounds for Open-Set Learning. CoRR abs/2106.15792 (2021) - [i22]Adi Lin, Jie Lu, Junyu Xuan, Fujin Zhu, Guangquan Zhang:
Deep Bayesian Estimation for Dynamic Treatment Regimes with a Long Follow-up Time. CoRR abs/2109.11929 (2021) - [i21]Junyu Xuan, Jie Lu, Guangquan Zhang:
Bayesian Transfer Learning: An Overview of Probabilistic Graphical Models for Transfer Learning. CoRR abs/2109.13233 (2021) - 2020
- [b4]Jie Lu, Qian Zhang, Guangquan Zhang:
Recommender Systems - Advanced Developments. Intelligent Information Systems 6, WorldScientific 2020, ISBN 9789811224621, pp. 1-380 - [j181]Yi Zhang, Mengjia Wu, Hua Lin, Steven Tipper, Mark Grosser, Guangquan Zhang, Jie Lu:
Framework of Computational Intelligence-Enhanced Knowledge Base Construction: Methodology and A Case of Gene-Related Cardiovascular Disease. Int. J. Comput. Intell. Syst. 13(1): 1109-1119 (2020) - [j180]Fujin Zhu, Jie Lu, Adi Lin, Guangquan Zhang:
A Pareto-smoothing method for causal inference using generalized Pareto distribution. Neurocomputing 378: 142-152 (2020) - [j179]Guanjin Wang, Guangquan Zhang, Kup-Sze Choi, Kin-Man Lam, Jie Lu:
Output based transfer learning with least squares support vector machine and its application in bladder cancer prognosis. Neurocomputing 387: 279-292 (2020) - [j178]Bin Wang, Tianrui Li, Zheng Yan, Guangquan Zhang, Jie Lu:
DeepPIPE: A distribution-free uncertainty quantification approach for time series forecasting. Neurocomputing 397: 11-19 (2020) - [j177]Guanjin Wang, Jeremy Yuen-Chun Teoh, Jie Lu, Kup-Sze Choi:
Least squares support vector machines with fast leave-one-out AUC optimization on imbalanced prostate cancer data. Int. J. Mach. Learn. Cybern. 11(8): 1909-1922 (2020) - [j176]Guanjin Wang, Jie Lu, Kup-Sze Choi, Guangquan Zhang:
A Transfer-Based Additive LS-SVM Classifier for Handling Missing Data. IEEE Trans. Cybern. 50(2): 739-752 (2020) - [j175]Yiliao Song, Jie Lu, Haiyan Lu, Guangquan Zhang:
Fuzzy Clustering-Based Adaptive Regression for Drifting Data Streams. IEEE Trans. Fuzzy Syst. 28(3): 544-557 (2020) - [j174]Jie Lu, Hua Zuo, Guangquan Zhang:
Fuzzy Multiple-Source Transfer Learning. IEEE Trans. Fuzzy Syst. 28(12): 3418-3431 (2020) - [j173]Adi Lin, Jie Lu, Junyu Xuan, Fujin Zhu, Guangquan Zhang:
A Causal Dirichlet Mixture Model for Causal Inference from Observational Data. ACM Trans. Intell. Syst. Technol. 11(3): 33:1-33:29 (2020) - [j172]Hang Yu, Jie Lu, Guangquan Zhang:
Online Topology Learning by a Gaussian Membership-Based Self-Organizing Incremental Neural Network. IEEE Trans. Neural Networks Learn. Syst. 31(10): 3947-3961 (2020) - [j171]Feng Liu, Guangquan Zhang, Jie Lu:
Heterogeneous Domain Adaptation: An Unsupervised Approach. IEEE Trans. Neural Networks Learn. Syst. 31(12): 5588-5602 (2020) - [j170]Junyu Xuan, Xiangfeng Luo, Jie Lu, Guangquan Zhang:
Web event evolution trend prediction based on its computational social context. World Wide Web 23(3): 1861-1886 (2020) - [c190]Hang Yu, Anjin Liu, Bin Wang, Ruimin Li, Guangquan Zhang, Jie Lu:
Real-Time Decision Making for Train Carriage Load Prediction via Multi-stream Learning. Australasian Conference on Artificial Intelligence 2020: 29-41 - [c189]Kun Wang, Anjin Liu, Jie Lu, Guangquan Zhang, Li Xiong:
An Elastic Gradient Boosting Decision Tree for Concept Drift Learning. Australasian Conference on Artificial Intelligence 2020: 420-432 - [c188]Feng Liu, Guangquan Zhang, Jie Lu:
A Novel Non-parametric Two-Sample Test on Imprecise Observations. FUZZ-IEEE 2020: 1-6 - [c187]Yiliao Song, Guangquan Zhang, Haiyan Lu, Jie Lu:
A Fuzzy Drift Correlation Matrix for Multiple Data Stream Regression. FUZZ-IEEE 2020: 1-6 - [c186]Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland:
Learning Deep Kernels for Non-Parametric Two-Sample Tests. ICML 2020: 6316-6326 - [c185]Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu:
Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation. IJCAI 2020: 2526-2532 - [c184]Qian Zhang, Jie Lu, Guangquan Zhang:
Cross-Domain Recommendation with Multiple Sources. IJCNN 2020: 1-7 - [c183]Keqiuyin Li, Jie Lu, Hua Zuo, Guangquan Zhang:
Multi-Source Domain Adaptation with Distribution Fusion and Relationship Extraction. IJCNN 2020: 1-6 - [c182]Anjin Liu, Guangquan Zhang, Kun Wang, Jie Lu:
Fast Switch Naïve Bayes to Avoid Redundant Update for Concept Drift Learning. IJCNN 2020: 1-7 - [c181]Hua Zuo, Jie Lu, Guangquan Zhang:
Multiple-source Domain Adaptation in Rule-based Neural Network. IJCNN 2020: 1-6 - [i20]Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland:
Learning Deep Kernels for Non-Parametric Two-Sample Tests. CoRR abs/2002.09116 (2020) - [i19]Jie Lu, Anjin Liu, Fan Dong, Feng Gu, João Gama, Guangquan Zhang:
Learning under Concept Drift: A Review. CoRR abs/2004.05785 (2020) - [i18]Anjin Liu, Jie Lu, Guangquan Zhang:
Diverse Instances-Weighting Ensemble based on Region Drift Disagreement for Concept Drift Adaptation. CoRR abs/2004.05810 (2020) - [i17]Anjin Liu, Jie Lu, Guangquan Zhang:
Concept Drift Detection via Equal Intensity k-means Space Partitioning. CoRR abs/2004.11587 (2020) - [i16]Li Zhong, Zhen Fang, Feng Liu, Bo Yuan, Guangquan Zhang, Jie Lu:
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation. CoRR abs/2006.13022 (2020) - [i15]Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu:
Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation. CoRR abs/2007.14612 (2020) - [i14]Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu:
Learning from a Complementary-label Source Domain: Theory and Algorithms. CoRR abs/2008.01454 (2020) - [i13]Anjin Liu, Jie Lu, Guangquan Zhang:
Concept Drift Detection: Dealing with MissingValues via Fuzzy Distance Estimations. CoRR abs/2008.03662 (2020)
2010 – 2019
- 2019
- [j169]Junyu Xuan, Jie Lu, Guangquan Zhang:
Cooperative hierarchical Dirichlet processes: Superposition vs. maximization. Artif. Intell. 271: 43-73 (2019) - [j168]Junyu Xuan, Jie Lu, Guangquan Zhang:
A Survey on Bayesian Nonparametric Learning. ACM Comput. Surv. 52(1): 13:1-13:36 (2019) - [j167]Ximeng Wang, Yun Liu, Jie Lu, Fei Xiong, Guangquan Zhang:
TruGRC: Trust-Aware Group Recommendation with Virtual Coordinators. Future Gener. Comput. Syst. 94: 224-236 (2019) - [j166]Mingsong Mao, Jie Lu, Jialin Han, Guangquan Zhang:
Multiobjective e-commerce recommendations based on hypergraph ranking. Inf. Sci. 471: 269-287 (2019) - [j165]Ruiping Yin, Kan Li, Guangquan Zhang, Jie Lu:
A deeper graph neural network for recommender systems. Knowl. Based Syst. 185 (2019) - [j164]