Keynote speeches to report research results of the Laureate Project
- KEYNOTE: Fuzzy Transfer Learning for Decision Making in Complex Environments, The 2021 IEEE International Conference on Progress in Informatics and Computing (PIC-2021), 17-19 December 2021, Shanghai. (virtual)
- KEYNOTE: Machine Learning for Decision Making in Complex Environments, EAI CloudComp 2021, 10 Dec 2021, Melbourne, Australia. (virtual)
- KEYNOTE: Concept Drift Detection, understanding and Adaptation 2021 IEEE International Conference on Digital Society and Intelligent Systems (IEEE-DSInS 2021), 3-4 December 2021, Chengdu, China. (virtual)
- KEYNOTE: Fuzzy Transfer Learning, International Conference on Computational Intelligence and Security (CIS'2021) 19-22 November 2021, Chengdu, China. (virtual)
- KEYNOTE: Fuzzy Transfer Learning, the 22nd International Conference on Web Information Systems Engineering (WISE), 26-29 October 2021, Melbourne, Australia. (virtual)
- KEYNOTE: Machine Learning for Decision Making in Complex Environments, IEEE Systems, Man, and Cybernetics (SMC) 2021, 17-20 October 2021, Melbourne, Australia. (virtual)
- KEYNOTE: Fuzzy Transfer Learning, IEEE Fellow Forum, AIExpo2021, 16. September 2021, Suzhou, China. (virtual)
Selected publications 2020 - 2021
- Fang, Z, Lu, J, Liu, F, Zhang, G. (2022) Semi-supervised Heterogeneous Domain Adaptation: Theory and Algorithms, Transactions on Pattern Analysis and Machine Intelligence (PAMI), DOI: 10.1109/ TPAMI.2022.3146234
- Liao, W. Zhang, Q., Yuan, B. Zhang, Lu, J. (2022) Heterogeneous Multi-domain Recommender System through Adversarial Learning, IEEE Transactions on Neural Networks and Learning Systems, 10.1109/TNNLS.2022.3154345
- Liu, A. Lu, J. Song, Y. Xuan, J. Zhang G. (2020) Concept Drift Detection Delay Index IEEE Transactions on Knowledge and Data Engineering, 10.1109/TKDE.2022. 3153349
- Wang, B. Lu, J. Li, T. Yan, Z. Zhang, G. (2022) A Quantile Fusion Methodology for Deep Forecasting, Neurocomputing, https://doi.org/10.1016/j.neucom.2022.02.029
- Li, K.; Lu, J.; Zuo, H.; Zhang, G. (2022) Dynamic Classifier Alignment for Unsupervised Multi-Source Domain Adaptation, IEEE Transactions on Knowledge and Data Engineering, 10.1109/TKDE.2022.3144423
- Yu, H.; Lu, J; Liu, A; Wang, B; Zhang, G; Li, R. (2021), Real-time Prediction System of Train Carriage Load Based on Multi-Stream Fuzzy Learning, IEEE Transactions on Intelligent Transportation Systems. DOI: 10.1109/TITS.2021.3137446
- Zhang, Q., Liao W., Zhang, G., Yuan, B., Lu, J. (2021) A Deep Dual Adversarial Network for Cross-domain Recommendation, Transactions on Knowledge and Data Engineering, 10.1109/ TKDE.2021.3132953
- Wang G. Wong KW. Lu, J (2021), AUC-Based Extreme Learning Machines for Supervised and Semi-Supervised Imbalanced Classification, IEEE Transactions on Systems, Man, and Cybernetics: Systems, DOI: 10.1109/TSMC.2020.2982226
- Liu, T. Lu, J. Yan, Z. Zhang, G. (2021) Statistical generalization performance guarantee for meta-learning with data dependent prior, Neurocomputing Vol. 465, 391-405
- Liu, Q., Geng, X., Huang, H., Qin, T., Lu, J. Jiang, D. (2021) "MGRC: An End-to-End Multi-Granularity Reading Comprehension Model for Question Answering", IEEE Transactions on Neural Networks and Learning Systems, DOI:10.1109/TNNLS.2021.3107029
- Song, Y., Lu, J., Lu, H. and Zhang, G. (2021), Learning data streams with changing distributions and temporal dependency, IEEE Transactions on Neural Networks and Learning Systems DOI: 10.1109/TNNLS.2021.3122531
- Zhong, L., Fang, Z., Liu, F., Yuan, B., Zhang, G. and Lu, J. (2021), Bridging the theoretical bound and deep algorithms for open set domain adaptation, IEEE Transactions on Neural Networks and Learning Systems DOI: 10.1109/TNNLS.2021.3119965
- Zhu, F., Lu, J., Lin, A., Xuan, J. and Zhang, G. (2020), Direct learning with multi-task neural networks for treatment effect estimation, IEEE Transactions on Knowledge and Data Engineering DOI: 10.1109/TKDE.2021.3112591
- Wang, K., Lu, J., Liu, A., Zhang, G. and Xiong, L. (2021), Evolving gradient boost: A pruning scheme based on loss improvement ratio for learning under concept drift, IEEE Transactions on Cybernetics DOI: 10.1109/TCYB.2021.3109796
- Liao, J. Zhou, J. Song, Y. Liu, B. Chen, Y, Wang, F. Chen, C. Lin, J. Chen, X. Lu, J. Jin D. (2021) Preselectable optical fingerprints of heterogeneous upconversion nanoparticles, Nano Letters Vol. 21, No. 18, 7659-7668
- Yu, H., Lu, J. Zhang, G. (2021), MORStreaming: A Multi-Output Regression System for Streaming Data, IEEE Transactions on Systems, Man and Cybernetics: Systems, DOI: 10.1109/TSMC.2021.3102978.
- Che, X., Zuo, H., Lu, J. and Chen, D. (2021), Fuzzy multi-output transfer learning for regression, IEEE Transactions on Fuzzy Systems DOI: 10.1109/TFUZZ.2021.3083956
- Zhang, Q., Lu, J. and Zhang, G. (2021), Recommender Systems in E-learning, Journal of Smart Environments and Green Computing Vol. 1, 76-89
- Zhang, Y., Liu, F., Fang, Z., Yuan, B., Zhang, G. and Lu, J. (2021), Learning from a complementary-label source domain: Theory and algorithms, IEEE Transactions on Neural Networks and Learning Systems DOI: 10.1109/TNNLS.2021.3086093
- Li, K., Lu, J., Zuo, H. and Zhang, G. (2021), Multi-source contribution learning for domain adaptation, IEEE Transactions on Neural Networks and Learning Systems DOI: 10.1109/TNNLS.2021.3069982
- Song, Y., Lu, J., Liu, A., Lu, H. and Zhang, G. (2021), A segment-based drift adaptation method for data streams, IEEE Transactions on Neural Networks and Learning Systems DOI: 10.1109/TNNLS.2021.3062062
- Dong, F., Lu, J., Song, Y., Liu, F. and Zhang, G. (2021), A drift Region-based data sample filtering method, IEEE Transactions on Cybernetics DOI: 10.1109/TCYB.2021.3051406
- Chang, W., Zhang, Q., Fu, C., Liu, W., Zhang, G. and Lu, J. (2021), A cross-domain recommender system through information transfer for medical diagnosis, Decision Support Systems Vol. 143, 113489
- Zhang, Y., Wu, M., Tian, G., Zhang, G. and Lu, J. (2021), Ethics and privacy of artificial intelligence: Understandings from bibliometrics, Knowledge-Based Systems Vol. 222, 106994
- Wu, M., Zhang, Y., Zhang, G. and Lu, J. (2021), Exploring the genetic basis of diseases through a heterogeneous bibliometric network: A methodology and case study, Technological Forecasting and Social Change Vol. 164, 120513.
- Yu, H., Lu, J., Zhang G. (2021) Topology Learning-based Fuzzy Random Neural Network for Streaming Data Regression, IEEE Transactions on Fuzzy Systems, DOI: 10.1109/TFUZZ.2020.3039681.
- Fang, Z., Lu, J., Liu, F., Xuan, J, Zhang G. (2021) Open Set Domain Adaptation: Theoretical Bound and Algorithm, IEEE Transactions on Neural Networks and Learning System Vol. 32, No. 10, 4309-4322
- Yu, H., Lu, J. and Zhang, G. (2020), Continuous support vector regression for nonstationary streaming data, IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2020.3015266.
- Liu, F., Zhang, G., and Lu, J. (2021) Multi-source heterogeneous unsupervised domain adaptation via fuzzy-relation neural networks, IEEE Transactions on Fuzzy Systems Vol. 29, No. 11, 3308-3322
- Liu, A., Lu, J. and Zhang, G. (2021), Concept drift detection: Dealing with missing values via fuzzy distance estimations, IEEE Transactions on Fuzzy Systems Vol. 19, No. 11, 3219-3233
- Wang, G., Choi, T., Teoh, J. and Lu, J. (2020), Deep cross-output knowledge transfer using stacked-structure least squares support vector machines, IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2020.3008963.
- Wang, G, Zhou T, Choi K, and Lu, J. (2020), A deep-ensemble-level based interpretable Takagi-Sugeno-Kang fuzzy classifier for imbalanced data, IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2020.3016972.
- Yu, H., Lu, J. and Zhang, G. (2020), An online robust support vector regression for data streams, IEEE Transactions on Knowledge and Data Engineering, DOI: 10.1109/TKDE.2020.2979967.
- Liu, A., Lu, J. and Zhang, G. (2021), Concept drift detection via equal intensity k-means space partitioning, IEEE Transactions on Cybernetics Vol. 51, No. 6, 3198-3211
- Liu, A., Lu, J. and Zhang, G. (2021), Diverse instance-weighting ensemble based on region drift disagreement for concept drift adaptation, IEEE Transactions on Neural Networks and Learning Systems Vol. 32, No. 1, 293-307
- Liu, F., Zhang, G. and Lu, J. (2020), Heterogeneous domain adaptation: An unsupervised approach, IEEE Transactions on Neural Networks and Learning Systems Vol. 31, No. 12, 5588-5602
- Liu, Q., Huang, H., Xuan, J., Zhang, G., Gao, Y. and Lu, J. (2021), A fuzzy word similarity measure for selecting top-k similar words, IEEE Transactions on Fuzzy Systems Vol. 29, No. 8, 2132-2144
- Liu, Q., Lu, J., Zhang, G., Shen, T., Zhang, Z., Huang, H. (2021), Domain-specific meta-embedding with latent semantic structures, Information Sciences Vol. 555,410-423
- Yang, K., Lu, J., Wan, W. and Zhang, G. (2021). Multi-source transfer regression via source-target pairwise segment, Information Sciences Vol. 556, 389-403
- Xuan, J., Lu, J, and Zhang, G. (2021). Bayesian nonparametric unsupervised concept drift detection for data stream mining, ACM Transactions on Intelligent Systems and Technology Vol. 12, No. 1, 1-22
- Siami, M., Naderpour, M. and Lu, J. (2021), A mobile telematics pattern recognition framework for driving behavior extraction, IEEE Transactions on Intelligent Transportation Systems Vol. 22, No. 3, 1459-1472
- Wang, M., Yan, Z., Wang, T., Cai, P., Gao, S., Zeng, Y., Wan, C., Wang, H., Pan, L., Yu, J., Pan, S., He, K., Lu, J. and Chen, X. (2020), Gesture recognition using a bioinspired learning architecture that integrates visual data with somatosensory data from stretchable sensors, Nature Electronics Vol. 3, No. 9, 563-570 (Highly Cited Paper)
- Zhang, Q., Lu, J., and Jin, Y. (2021). Artificial intelligence in recommender systems. Complex & Intelligent Systems Vol. 7, No. 1, 439-457
- Lin, A., Lu, J., Xuan, J., Zhu, F. and Zhang, G. (2020), A causal Dirichlet mixture model for causal inference from observational data, ACM Transactions on Intelligent Systems and Technology, Vol. 11, No. 3, pp. 1-29.
- Lu, J., Liu, A., Song, Y. and Zhang, G. (2020), Data-driven decision support under concept drift in streamed big data, Complex & Intelligent Systems, Vol. 6, No. 1, pp. 157-163.
- Wang, B., Li, T., Yan, Z., Zhang, G. and Lu, J. (2020), DeepPIPE: A distribution-free uncertainty quantification approach for time series forecasting, Neurocomputing, Vol. 397, pp. 11-19.
- Xuan, J., Luo, X., Lu, J. and Zhang, G. (2020), Web event evolution trend prediction based on its computational social context, World Wide Web, Vol. 23, pp. 1861-1886.
- Zhu, F., Lu, J., Lin, A. and Zhang, G. (2020), A Pareto-smoothing method for causal inference using generalized Pareto distribution, Neurocomputing, Vol. 378, pp. 142-152.
- Song, Y., Lu, J., Lu, H. and Zhang, G. (2020), Fuzzy clustering-based adaptive regression for drifting data streams, IEEE Transactions on Fuzzy Systems, Vol. 28, No. 3, pp. 544-557.
- Wang, G., Lu, J., Choi, K.S. and Zhang, G. (2020), A transfer-based additive LS-SVM classifier for handling missing data, IEEE Transactions on Cybernetics, Vol. 50, No. 2, pp. 739-752.