Feng Liu (Postdoctoral Researcher at UTS-AAII)


Publications


Currently (since July 2016), I research the machine learning methods (mainly focus on domain adaptation/transfer learning) and two-sample testing (an important mathematical tool in machine learning and statistics). Previously (2013-2016), I researched the time series prediction methods using neural networks. In the following, represents equal contribution, and * represents corresponding author.

[Conference Papers, Selected Journal Articles, Theses ]


Working Papers

  1. F. Liu, J. Lu, A. Liu and G. Zhang.
    Discrepancy of Diverse Subsets for Distribution Comparison.
    IEEE Transactions on Pattern Analysis and Machine Intelligence, Under Review, 2018 (ERA A*).

  2. H. Chi, F. Liu, W. Yang, L. Lan and B. Han.
    TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation.

  3. R. Gao, F. Liu, J. Zhang, B. Han, T. Liu, G. Niu and M. Sugiyama.
    Maximum Mean Discrepancy is Aware of Adversarial Attacks.
    AISTATS 2021, Under Review, 2020.
    [ arXiv ]

  4. Z. Fang, J. Lu, F. Liu, G. Zhang.
    Semi-supervised Heterogeneous Domain Adaptation: Theory and Algorithms.
    IEEE Transactions on Pattern Analysis and Machine Intelligence, Under Review, 2020 (ERA A*).


Conference Papers (including workshops)

  1. L. Zhong, Z. Fang, F. Liu, B. Yuan, G. Zhang and J. Lu.
    How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?
    In AAAI Conference on Artificial Intelligence (AAAI 2021), To appear (CORE A*).

  2. F. Liu, W. Xu, J. Lu, G. Zhang and A. Gretton, D. J. Sutherland.
    Learning Deep Kernels for Non-parametric Two Sample Test.
    In International Conference on Machine Learning (ICML 2020), online, 2020 (CORE A*).
    [ arXiv ] [ CODE ]

  3. F. Liu, G. Zhang and J. Lu.
    A Novel Non-parametric Two-Sample Test on Imprecise Observations.
    In IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2020), online, 2020 (CORE A).

  4. Y. Zhang, F. Liu, Z. Fang, B. Yuan, G. Zhang and J. Lu.
    Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation.
    In International Joint Conference on Artificial Intelligence (IJCAI 2020), to appear (CORE A*).
    [ arXiv ] [ CODE ]

  5. F. Liu, J. Lu, B. Han, G. Niu, G. Zhang and M. Sugiyama.
    Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation.
    In Learning Transferable Skills Workshop on Neural Information Processing Systems (NeurIPS 2019 Workshop), Vancouver, Canada, December 8-14, 2019 (CORE A*).
    [ PDF ]

  6. F. Liu, G. Zhang and J. Lu.
    A Novel Fuzzy Neural Network for Unsupervised Domain Adaptation in Heterogeneous Scenarios.
    In IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2019), New Orleans, USA, June 23-26, 2019 (CORE A).
    [ link ][ Best Student Paper Award ]

  7. Z. Fang, J. Lu, F. Liu and G. Zhang.
    Unsupervised Domain Adaptation with Sphere Retracting Transformation.
    In International Joint Conference on Neural Networks (IJCNN 2019), Budapest, Hungary, July 14-19, 2019 (CORE A).
    [ link ]

  8. F. Liu, G. Zhang and J. Lu.
    Unconstrained Fuzzy Feature Fusion for Heterogeneous Unsupervised Domain Adaptation.
    In IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2018), Rio de Janeiro, Brazil, July 8-13, 2018 (CORE A).
    [ link ]

  9. F. Liu, G. Zhang and J. Lu.
    Heterogeneous Unsupervised Domain Adaptation based on Fuzzy Feature Fusion.
    In IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2017), Naples, Italy, July 9-12, 2017 (CORE A).
    [ link ]


Selected Journal Articles

  1. F. Liu, J. Lu, B. Han, G. Niu, G. Zhang and M. Sugiyama.
    Butterfly: One-step Approach towards Wildly Unsupervised Domain Adaptation.
    IEEE Transactions on Pattern Analysis and Machine Intelligence, Revise and Resubmit, 2020 (ERA A*).
    [ arXiv ]

  2. F. Liu, G. Zhang and J. Lu.
    Multi-source Heterogeneous Unsupervised Domain Adaptation via Fuzzy-relation Neural Networks.
    IEEE Transactions on Fuzzy Systems, 2020 (ERA A*).
    [ link ]

  3. F. Liu, G. Zhang and J. Lu.
    Heterogeneous domain adaptation: An unsupervised approach.
    IEEE Transactions on Neural Networks and Learning Systems, 2020 (ERA A*).
    [ arXiv ]

  4. Y. Zhang, F. Liu, Z. Fang, B. Yuan, G. Zhang and J. Lu.
    Learning from a Complementary-label Source Domain: Theory and Algorithms.
    IEEE Transactions on Neural Networks and Learning Systems, Major Revision, 2020 (ERA A*).
    [ arXiv ]

  5. S. Qin, H. Ding, Y. Wu and F. Liu.
    High-dimensional sign-constrained feature selection and grouping.
    Annals of the Institute of Statistical Mathematics, Accepted, 2020 (ERA A).
    [ link ]

  6. L. Zhong, Z. Fang, F. Liu, B. Yuan, G. Zhang and J. Lu.
    Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation.
    IEEE Transactions on Neural Networks and Learning Systems, Minor Revision, 2020 (ERA A*).
    [ arXiv ]

  7. Z. Fang, J. Lu, F. Liu, J. Xuan and G. Zhang.
    Open set domain adaptation: Theoretical bound and algorithm.
    IEEE Transactions on Neural Networks and Learning Systems, 2020 (ERA A*).
    [ arXiv ] [ CODE ]

  8. F. Dong, J. Lu, Y. Song, F. Liu and G. Zhang.
    A Concept Drift Region-based Data Sample Editing Methodology.
    IEEE Transactions on Cybernetics, Major Revision, 2020 (ERA A).

  9. F. Liu, J. Lu and G. Zhang.
    Unsupervised heterogeneous domain adaptation via shared fuzzy equivalence relations.
    IEEE Transactions on Fuzzy Systems, vol. 26, no. 6, pp. 3555–-3568, 2018 (ERA A*).
    [ link ] [ CODE ]

  10. H. Zuo, J. Lu, G. Zhang and F. Liu.
    Fuzzy transfer learning using an infinite gaussian mixture model and active learning.
    IEEE Transactions on Fuzzy Systems, vol. 27, no. 2, pp. 291–-303, 2018 (ERA A*).
    [ link ]

  11. A. Liu, J. Lu, F. Liu and G. Zhang.
    Accumulating regional density dissimilarity for concept drift detection in data streams.
    Pattern Recognition, vol. 76, pp. 256--272, 2018 (ERA A*).
    [ link ] [ CODE ]

  12. Y. Zhang, J. Lu, F. Liu, Q. Liu, A. Porter, H. Chen and G. Zhang.
    Does deep learning help topic extraction? A kernel k-means clustering method with word embedding.
    Journal of Informetrics, vol. 12, no. 4, pp. 1099–-1117, 2018 (ERA A).
    [ link ]

  13. Q. Zhang, D. Wu, J. Lu, F. Liu and G. Zhang.
    A cross-domain recommender system with consistent information transfer.
    Decision Support Systems, vol. 104, pp. 49--63, 2017 (ERA A*).
    [ link ]

  14. P. Jiang, F. Liu* and Y. Song.
    A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting.
    Energy, vol. 119, pp. 694--709, 2017 (JCR Q1).
    [ link ]

  15. P. Jiang, F. Liu* and Y. Song.
    Cuckoo search-designated fractal interpolation functions with winner combination for estimating missing values in time series.
    Applied Mathematical Modelling, vol. 40, no. 23--24, pp. 9692--9718, 2016 (JCR Q1).
    [ link ]

  16. J. Wang, F. Liu*, Y. Song and J. Zhao.
    A novel model: Dynamic choice artificial neural network (DCANN) for an electricity price forecasting system.
    Applied Soft Computing, vol. 48, pp. 281--297, 2016 (JCR Q1).
    [ link ]

  17. J. Zhao, Z. Guo, Z. Su, Z. Zhao, X. Xiao and F. Liu*.
    An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed.
    Applied energy, vol. 162, pp. 808--826, 2016 (JCR Q1).
    [ link ] [ Highly-cited paper, Total citations: 142 ]

  18. S. Qin, F. Liu*, C. Wang, Y. Song and J. Qu.
    Spatial-temporal analysis and projection of extreme particulate matter (PM10 and PM2. 5) levels using association rules: A case study of the Jing-Jin-Ji region, China.
    Atmospheric Environment, vol. 120, pp. 339--350, 2015 (JCR Q1).
    [ link ]

  19. S. Qin, F. Liu*, J. Wang and B. Sun.
    Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models.
    Atmospheric Environment, vol. 98, pp. 665--675, 2014 (JCR Q1).
    [ link ]

  20. Z. Wang, F. Liu*, J. Wu and J. Wang.
    A Hybrid Forecasting Model Based on Bivariate Division and a Backpropagation Artificial Neural Network Optimized by Chaos Particle Swarm Optimization for Day-Ahead Electricity Price.
    Abstract and Applied Analysis, ArticleID: 249208, 2014 (JCR Q1).
    [ link ]


Theses

  1. Feng Liu.
    Towards Realistic Transfer Learning Methods: Theory and Algorithms.
    Doctoral Thesis, Australian Artificial Intelligence Institute, University of Technology Sydney, Australia, November 2020.

  2. Feng Liu.
    Time Series Interpolation and Prediction for the Electricity Market.
    Master Thesis, School of Mathematic and Statistics, Lanzhou University, China, June 2015.