Dr Xiaochen Yang

  • Lecturer (Statistics)

Research interests

My research interests lie in statistical machine learning and image analysis. Past works include distance metric learning and target detection for hyperspectral images. The ongoing research topics include: (1) machine learning under data paucity (or learning with small data): few-shot learning, self-supervised learning, domain adaptation; (2) trustworthy machine learning: adversarial attacks and defenses, uncertainty quantification, interpretability; (3) machine learning for medical and healthcare applications: medical image analysis, disease detection and diagnosis, disease progression modeling.

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

Research groups

Publications

List by: Type | Date

Jump to: 2024 | 2023 | 2022 | 2021 | 2020 | 2019
Number of items: 21.

2024

Dong, F. et al. (2024) Once Read is Enough: Domain-specific Pretraining-free Language Models with Cluster-guided Sparse Experts for Long-tail Domain Knowledge. In: 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024), Vancouver, Canada, 9-15 December 2024, (Accepted for Publication)

Li, X., Li, Z., Xie, J., Yang, X. , Xue, J.-H. and Ma, Z. (2024) Self-reconstruction network for fine-grained few-shot classification. Pattern Recognition, 153, 110485. (doi: 10.1016/j.patcog.2024.110485)

Di Campli San Vito, P. et al. (2024) RadioMe: Adaptive Radio with Music Intervention and Reminder System for People with Dementia in Their Own Home. In: Augmented Humans 2024, Melbourne, Australia, 4-6 April 2024, ISBN 9798400709807 (doi: 10.1145/3652920.3653055)

Yang, X. , Guo, Y., Dong, M. and Xue, J.-H. (2024) Towards certified robustness of distance metric learning. IEEE Transactions on Neural Networks and Learning Systems, 35(3), pp. 3834-3844. (doi: 10.1109/TNNLS.2022.3199902) (PMID:36112549)

2023

Cheng, F. and Yang, X. (2023) Self-Supervised Cross-Encoder for Diagnosis of Alzheimer's Disease. 37th annual conference on Neural Information Processing Systems, New Orleans, Louisiana, USA, 10-16 Dec 2023. (Accepted for Publication)

Di Campli San Vito, P., Shakeri, G., Ross, J., Yang, X. and Brewster, S. (2023) Development of a Real-Time Stress Detection System for Older Adults with Heart Rate Data. In: 16th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA '23), Corfu, Greece, 5-7 Jul 2023, ISBN 9798400700699 (doi: 10.1145/3594806.3594817)

Colombo, P., Miller, C. , O'Donnell, R. and Yang, X. (2023) A Multifidelity Framework for Wind Speed Data. In: 37th International Workshop on Statistical Modelling (IWSM), Dortmund, Germany, 16-21 Jul 2023, ISBN 9783947323425

Di Campli San Vito, P. et al. (2023) RadioMe: Adaptive Radio to Support People with Mild Dementia in Their Own Home. In: Second International Conference on Hybrid Human-Artificial Intelligence (HHAI 2023), Munich, Germany, 26-30 June 2023, pp. 413-415. ISBN 9781643683942 (doi: 10.3233/FAIA230114)

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,

Shi, Y. et al. (2023) Train Faster, Perform Better: Modular Adaptive Training in Over-Parameterized Models. In: 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, Louisiana, USA, 10-16 Dec 2023,

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)

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 Mon Jan 6 16:29:51 2025 GMT.
Jump to: Articles | Conference or Workshop Item | Conference Proceedings
Number of items: 21.

Articles

Li, X., Li, Z., Xie, J., Yang, X. , Xue, J.-H. and Ma, Z. (2024) Self-reconstruction network for fine-grained few-shot classification. Pattern Recognition, 153, 110485. (doi: 10.1016/j.patcog.2024.110485)

Yang, X. , Guo, Y., Dong, M. and Xue, J.-H. (2024) Towards certified robustness of distance metric learning. IEEE Transactions on Neural Networks and Learning Systems, 35(3), pp. 3834-3844. (doi: 10.1109/TNNLS.2022.3199902) (PMID:36112549)

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)

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 or Workshop Item

Cheng, F. and Yang, X. (2023) Self-Supervised Cross-Encoder for Diagnosis of Alzheimer's Disease. 37th annual conference on Neural Information Processing Systems, New Orleans, Louisiana, USA, 10-16 Dec 2023. (Accepted for Publication)

Conference Proceedings

Dong, F. et al. (2024) Once Read is Enough: Domain-specific Pretraining-free Language Models with Cluster-guided Sparse Experts for Long-tail Domain Knowledge. In: 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024), Vancouver, Canada, 9-15 December 2024, (Accepted for Publication)

Di Campli San Vito, P. et al. (2024) RadioMe: Adaptive Radio with Music Intervention and Reminder System for People with Dementia in Their Own Home. In: Augmented Humans 2024, Melbourne, Australia, 4-6 April 2024, ISBN 9798400709807 (doi: 10.1145/3652920.3653055)

Di Campli San Vito, P., Shakeri, G., Ross, J., Yang, X. and Brewster, S. (2023) Development of a Real-Time Stress Detection System for Older Adults with Heart Rate Data. In: 16th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA '23), Corfu, Greece, 5-7 Jul 2023, ISBN 9798400700699 (doi: 10.1145/3594806.3594817)

Colombo, P., Miller, C. , O'Donnell, R. and Yang, X. (2023) A Multifidelity Framework for Wind Speed Data. In: 37th International Workshop on Statistical Modelling (IWSM), Dortmund, Germany, 16-21 Jul 2023, ISBN 9783947323425

Di Campli San Vito, P. et al. (2023) RadioMe: Adaptive Radio to Support People with Mild Dementia in Their Own Home. In: Second International Conference on Hybrid Human-Artificial Intelligence (HHAI 2023), Munich, Germany, 26-30 June 2023, pp. 413-415. ISBN 9781643683942 (doi: 10.3233/FAIA230114)

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,

Shi, Y. et al. (2023) Train Faster, Perform Better: Modular Adaptive Training in Over-Parameterized Models. In: 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, Louisiana, USA, 10-16 Dec 2023,

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 Mon Jan 6 16:29:51 2025 GMT.

Supervision

  • 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 read the following page for more information. 

https://xiao-chen-yang.github.io/phd