Publications

Works listed as first/corresponding author (*) and equal contributions (#); see Google Scholar.

Preprints

1. Yijun Bian* and Lei You, "Fairness research for machine learning should integrate societal considerations," arXiv preprint arXiv:2506.12556, 2025. Under Review. [arXiv]

2. Lin Zhu#, Yijun Bian#, and Lei You*, "FairSHAP: Preprocessing for fairness through attribution-based data augmentation," arXiv preprint arXiv:2505.11111, 2025. Under Review. [arXiv][code]

3. Alina Basharat, Yijun Bian, Ping Xu*, and Zhi Tian, "Towards trustworthy federated learning," arXiv preprint arXiv:2503.03684, 2025. [arXiv]

4. Lei You*, Yijun Bian, and Lele Cao, "Refining counterfactual explanations with joint-distribution-informed Shapley towards actionable minimality," arXiv preprint arXiv:2410.05419, 2024. Under Review. [arXiv][code][software]

5. Yijun Bian#*, Yujie Luo#*, and Ping Xu, "Approximating discrimination within models when faced with several non-binary sensitive attributes," arXiv preprint arXiv:2408.06099, 2024. Under Review. [arXiv][code]

6. Yijun Bian#* and Yujie Luo#, "Does machine bring in extra bias in learning? Approximating fairness in models promptly," arXiv preprint arXiv:2405.09251, 2024. [arXiv][code]

7. Jinghan Huang#, Qiufeng Chen#, Yijun Bian, Pengli Zhu, Nanguang Chen, Moo K. Chung, and Anqi Qiu*, "Advancing graph neural networks with HL-HGAT: A Hodge-Laplacian and attention mechanism approach for heterogeneous graph-structured data," arXiv preprint arXiv:2403.06687, 2024. Under Review. [arXiv][code]

8. Yijun Bian* and Kun Zhang, "Increasing fairness via combination with learning guarantees," arXiv preprint arXiv:2301.10813, 2023. Under Review. [arXiv]

9. Abhijith Sharma#, Yijun Bian#*, Phil Munz, and Apurva Narayan, "Adversarial patch attacks and defences in vision-based tasks: A survey," arXiv preprint arXiv:2206.08304, 2022. [arXiv][TechRxiv]

Non-archival posters

• Yijun Bian and Kun Zhang, "Increasing fairness via combination with learning guarantees," in NeurIPS 2024 Workshop on Mathematics of Modern Machine Learning (M3L), Dec 2024. [paper][poster]

• Yijun Bian#, Yujie Luo#, and Ping Xu, "Does machine bring in extra bias in learning? Approximating discrimination within models quickly," in NeurIPS 2024 Workshop on Mathematics of Modern Machine Learning (M3L), Dec 2024. [paper][poster]

Journal articles

1. Ming Chen, Yijun Bian, Nanguang Chen, and Anqi Qiu*, "Orthogonal mixed-effects modeling for high-dimensional longitudinal data: An unsupervised learning approach," IEEE Transactions on Medical Imaging, vol. 44, no. 1, pp. 207-220, Jan 2025. [paper][code][doi]

2. Yijun Bian, Qingquan Song, Mengnan Du, Jun Yao, Huanhuan Chen*, and Xia Hu, "Subarchitecture ensemble pruning in neural architecture search," IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 12, pp. 7928-7936, Dec 2022. [arXiv][paper][code][doi]

3. Yijun Bian and Huanhuan Chen*, "When does diversity help generalization in classification ensembles?" IEEE Transactions on Cybernetics, vol. 52, no. 9, pp. 9059–9075, Sept 2022. [arXiv][paper][doi]

4. Yijun Bian, Yijun Wang, Yaqiang Yao, and Huanhuan Chen*, "Ensemble pruning based on objection maximization with a general distributed framework," IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 9, pp. 3766–3774, Sept 2020. [arXiv][paper][code][doi]

Conference paper

1. Abhijith Sharma, Yijun Bian, Vatsal Nanda, Phil Munz, and Apurva Narayan, "Vulnerability of CNNs against multi-patch attacks," in Proceedings of the 2023 ACM Workshop on Secure and Trustworthy Cyber-Physical Systems (SaT-CPS’23), pp. 23–32, Apr 2023. [paper][doi]

Released code of projects

• ApproxBias, official released code for "Does machine bring in extra bias in learning? Approximating fairness in models promptly" and "Approximating discrimination within models when faced with several non-binary sensitive attributes".

• SAEP, official released code for "Subarchitecture ensemble pruning in neural architecture search".

• EPFD, official released code for "Ensemble pruning based on objection maximisation with a general distributed framework".

• PyEnsemble, an open-source library for ensemble learning methods, diversity measures, and ensemble pruning methods.