Keynote speeches and selected publications

Keynote speeches to report research results of the Laureate Project

  1. 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)
  2. KEYNOTE: Machine Learning for Decision Making in Complex Environments, EAI CloudComp 2021, 10 Dec 2021, Melbourne, Australia. (virtual)
  3. 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)
  4. KEYNOTE: Fuzzy Transfer Learning, International Conference on Computational Intelligence and Security (CIS'2021) 19-22 November 2021, Chengdu, China. (virtual)
  5. KEYNOTE: Fuzzy Transfer Learning, the 22nd International Conference on Web Information Systems Engineering (WISE), 26-29 October 2021, Melbourne, Australia. (virtual)
  6. KEYNOTE: Machine Learning for Decision Making in Complex Environments, IEEE Systems, Man, and Cybernetics (SMC) 2021, 17-20 October 2021, Melbourne, Australia. (virtual)
  7. KEYNOTE: Fuzzy Transfer Learning, IEEE Fellow Forum, AIExpo2021, 16. September 2021, Suzhou, China. (virtual)

Selected publications 2020 - 2021

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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.
  17. 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
  18. Zhang, Q., Lu, J. and Zhang, G. (2021), Recommender Systems in E-learning, Journal of Smart Environments and Green Computing Vol. 1, 76-89
  19. 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
  20. 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
  21. 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 
  22. 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  
  23. 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
  24. 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
  25. 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.
  26. 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.
  27. 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
  28. 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.
  29. 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
  30. 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
  31. 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.
  32. 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.
  33. 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.   
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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)
  43. Zhang, Q., Lu, J., and Jin, Y. (2021). Artificial intelligence in recommender systems. Complex & Intelligent Systems Vol. 7, No. 1, 439-457
  44. 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.
  45. 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.
  46. 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.
  47. 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.
  48. 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.
  49. 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.
  50. 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.
Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.