89 citations · 171 across the 7 of their papers we have counts for
7 papers
Approximating Discrimination Within Models When Faced With Several Non-Binary Sensitive Attributes
Yijun Bian, Yujie Luo, Ping Xu
Discrimination mitigation within machine learning (ML) models could be complicated because multiple factors may be interwoven hierarchically and historically. Yet few existing fair…
Measuring Model-Induced Discrimination via Efficient Fairness Approximation
Yijun Bian, Yujie Luo
Providing various machine learning (ML) applications in the real world, concerns about discrimination hidden in ML models are growing, particularly in high-stakes domains. Existing…
Improving Fairness with Ensemble Combination: Margin-Dependent Bounds
Yijun Bian
The concern about hidden discrimination in machine learning models is growing, as their widespread real-world applications increasingly impact human lives. Various techniques, incl…
Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Survey
Abhijith Sharma, Yijun Bian, Phil Munz +1
Adversarial attacks in deep learning models, especially for safety-critical systems, are gaining more and more attention in recent years, due to the lack of trust in the security a…
When does Diversity Help Generalization in Classification Ensembles?
Yijun Bian, Huanhuan Chen
Ensembles, as a widely used and effective technique in the machine learning community, succeed within a key element -- "diversity." The relationship between diversity and generaliz…
Sub-Architecture Ensemble Pruning in Neural Architecture Search
Yijun Bian, Qingquan Song, Mengnan Du +3
Neural architecture search (NAS) is gaining more and more attention in recent years due to its flexibility and remarkable capability to reduce the burden of neural network design.…
Ensemble Pruning based on Objection Maximization with a General Distributed Framework
Yijun Bian, Yijun Wang, Yaqiang Yao +1
Ensemble pruning, selecting a subset of individual learners from an original ensemble, alleviates the deficiencies of ensemble learning on the cost of time and space. Accuracy and…