Dr Xiaochen Yang

  • Lecturer (Statistics)

Research interests

My research interests lie in statistical machine learning and image analysis. Past work includes developing target detection methods for hyperspectral images. Currently I am working on distance metric learning, improving methods towards better generalization in practically challenging scenarios, such as the few-shot regime and learning from weak supervision, and stronger (adversarial) robustness. I am interested in environmental and medical applications.

Personal webpage: https://xiao-chen-yang.github.io/

Research units

Publications

List by: Type | Date

Jump to: 2023 | 2022 | 2021 | 2020 | 2019
Number of items: 13.

2023

Li, X., Yang, X. , Ma, Z. and Xue, J.-H. (2023) Deep metric learning for few-shot image classification: a review of recent developments. Pattern Recognition, 138, 109381. (doi: 10.1016/j.patcog.2023.109381)

Chen, Y. et al. (2023) Over-parameterized Model Optimization with Polyak-Łojasiewicz Condition. In: 11th International Conference on Learning Representations (ICLR 2023), Kigali, Rwanda, 1-5 May 2023, (Accepted for Publication)

2022

Li, Z., Wang, L., Ding, S., Yang, X. and Li, X. (2022) Few-Shot Classification With Feature Reconstruction Bias. In: 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Chiang Mai, Thailand, 7-10 November 2022, pp. 526-532. ISBN 9781665486620 (doi: 10.23919/APSIPAASC55919.2022.9980086)

Song, Q., Peng, Z., Ji, L., Yang, X. and Li, X. (2022) Dual Prototypical Network for Robust Few-shot Image Classification. In: 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Chiang Mai, Thailand, 7-10 November 2022, pp. 533-537. ISBN 9781665486620 (doi: 10.23919/APSIPAASC55919.2022.9979898)

Yang, X. , Guo, Y., Dong, M. and Xue, J.-H. (2022) Towards certified robustness of distance metric learning. IEEE Transactions on Neural Networks and Learning Systems, (doi: 10.1109/TNNLS.2022.3199902) (PMID:36112549) (Early Online Publication)

Lu, Y., Wang, B., Zhao, Y., Yang, X. , Li, L., Dong, M., Lv, Q., Zhou, F., Gu, N. and Shang, L. (2022) Physics-informed surrogate modeling for hydro-fracture geometry prediction based on deep learning. Energy, 253, 124139. (doi: 10.1016/j.energy.2022.124139)

2021

Li, X., Yu, L., Yang, X. , Ma, Z., Xue, J.-H., Cao, J. and Guo, J. (2021) ReMarNet: conjoint relation and margin learning for small-sample image classification. IEEE Transactions on Circuits and Systems for Video Technology, 31(4), pp. 1569-1579. (doi: 10.1109/TCSVT.2020.3005807)

Yang, X. , Dong, M., Guo, Y. and Xue, J.-H. (2021) Metric Learning for Categorical and Ambiguous Features: An Adversarial Approach. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2020), 14-18 Sep 2020, pp. 223-238. ISBN 9783030676605 (doi: 10.1007/978-3-030-67661-2_14)

2020

Li, X., Yan, J., Wu, J., Liu, Y., Yang, X. and Ma, Z. (2020) Anti-Noise Relation Network for Few-shot Learning. In: 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Auckland, New Zealand, 07-10 Dec 2020, pp. 1719-1724. ISBN 9789881476883

Yang, X. , Dong, M., Wang, Z., Gao, L., Zhang, L. and Xue, J.-H. (2020) Data-augmented matched subspace detector for hyperspectral subpixel target detection. Pattern Recognition, 106, 107464. (doi: 10.1016/j.patcog.2020.107464)

Dong, M., Wang, Y., Yang, X. and Xue, J.-H. (2020) Learning local metrics and influential regions for classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(6), pp. 1522-1529. (doi: 10.1109/TPAMI.2019.2914899)

Dong, M., Yang, X. , Zhu, R., Wang, Y. and Xue, J.-H. (2020) Generalization Bound of Gradient Descent for Non-Convex Metric Learning. In: 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 06-12 Dec 2020,

2019

Yang, X. , Zhang, L., Gao, L. and Xue, J.-H. (2019) MSDH: matched subspace detector with heterogeneous noise. Pattern Recognition Letters, 125, pp. 701-707. (doi: 10.1016/j.patrec.2019.07.014)

This list was generated on Tue Mar 21 11:39:13 2023 GMT.
Jump to: Articles | Conference Proceedings
Number of items: 13.

Articles

Li, X., Yang, X. , Ma, Z. and Xue, J.-H. (2023) Deep metric learning for few-shot image classification: a review of recent developments. Pattern Recognition, 138, 109381. (doi: 10.1016/j.patcog.2023.109381)

Yang, X. , Guo, Y., Dong, M. and Xue, J.-H. (2022) Towards certified robustness of distance metric learning. IEEE Transactions on Neural Networks and Learning Systems, (doi: 10.1109/TNNLS.2022.3199902) (PMID:36112549) (Early Online Publication)

Lu, Y., Wang, B., Zhao, Y., Yang, X. , Li, L., Dong, M., Lv, Q., Zhou, F., Gu, N. and Shang, L. (2022) Physics-informed surrogate modeling for hydro-fracture geometry prediction based on deep learning. Energy, 253, 124139. (doi: 10.1016/j.energy.2022.124139)

Li, X., Yu, L., Yang, X. , Ma, Z., Xue, J.-H., Cao, J. and Guo, J. (2021) ReMarNet: conjoint relation and margin learning for small-sample image classification. IEEE Transactions on Circuits and Systems for Video Technology, 31(4), pp. 1569-1579. (doi: 10.1109/TCSVT.2020.3005807)

Yang, X. , Dong, M., Wang, Z., Gao, L., Zhang, L. and Xue, J.-H. (2020) Data-augmented matched subspace detector for hyperspectral subpixel target detection. Pattern Recognition, 106, 107464. (doi: 10.1016/j.patcog.2020.107464)

Dong, M., Wang, Y., Yang, X. and Xue, J.-H. (2020) Learning local metrics and influential regions for classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(6), pp. 1522-1529. (doi: 10.1109/TPAMI.2019.2914899)

Yang, X. , Zhang, L., Gao, L. and Xue, J.-H. (2019) MSDH: matched subspace detector with heterogeneous noise. Pattern Recognition Letters, 125, pp. 701-707. (doi: 10.1016/j.patrec.2019.07.014)

Conference Proceedings

Chen, Y. et al. (2023) Over-parameterized Model Optimization with Polyak-Łojasiewicz Condition. In: 11th International Conference on Learning Representations (ICLR 2023), Kigali, Rwanda, 1-5 May 2023, (Accepted for Publication)

Li, Z., Wang, L., Ding, S., Yang, X. and Li, X. (2022) Few-Shot Classification With Feature Reconstruction Bias. In: 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Chiang Mai, Thailand, 7-10 November 2022, pp. 526-532. ISBN 9781665486620 (doi: 10.23919/APSIPAASC55919.2022.9980086)

Song, Q., Peng, Z., Ji, L., Yang, X. and Li, X. (2022) Dual Prototypical Network for Robust Few-shot Image Classification. In: 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Chiang Mai, Thailand, 7-10 November 2022, pp. 533-537. ISBN 9781665486620 (doi: 10.23919/APSIPAASC55919.2022.9979898)

Yang, X. , Dong, M., Guo, Y. and Xue, J.-H. (2021) Metric Learning for Categorical and Ambiguous Features: An Adversarial Approach. In: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2020), 14-18 Sep 2020, pp. 223-238. ISBN 9783030676605 (doi: 10.1007/978-3-030-67661-2_14)

Li, X., Yan, J., Wu, J., Liu, Y., Yang, X. and Ma, Z. (2020) Anti-Noise Relation Network for Few-shot Learning. In: 2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Auckland, New Zealand, 07-10 Dec 2020, pp. 1719-1724. ISBN 9789881476883

Dong, M., Yang, X. , Zhu, R., Wang, Y. and Xue, J.-H. (2020) Generalization Bound of Gradient Descent for Non-Convex Metric Learning. In: 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 06-12 Dec 2020,

This list was generated on Tue Mar 21 11:39:13 2023 GMT.

Supervision

I am currently looking for highly motivated PhD students. Please email me if you are interested.

  • Colombo, Pietro
    Methodological developments for accounting for uncertainty within environmental data
  • Mandal, Adhiraj
    An Investigation into Distribution of Random Functions and Model-Based Clustering for Functional Data

Teaching

Statistics 2Y: Regression Modelling (STATS2006)

Data Mining and Machine Learning (STATS5099)

Additional information

I am currently looking for highly motivated PhD students. Please email me if you are interested.