Shengfei Wei — accepted AI-conference papers

2026

A General Anchor-Based Framework for Scalable Fair Clustering

Shengfei Wei, Suyuan Liu, Jun Wang, Ke Liang, Miaomiao Li, Lei Luo

AAAI 2026technical

Fair clustering is crucial for mitigating bias in unsupervised learning, yet existing algorithms often suffer from quadratic or super-quadratic computational complexity, rendering them impractical for large-scale datasets. To bridge this gap, we introduce the Anchor-based Fair Clustering Framework (

Cited by 0 Source ↗PDF ↗Scholar ↗

2026

Causal Disentangled Anchor Learning for Scalable Fair Multi-view Clustering

Suyuan Liu, Shengfei Wei, Wenjing Yang, Shengju Yu, Siwei Wang, Xueqiong Li +2

ICML 2026poster

Existing fair multi-view clustering methods typically suffer from a severe trade-off between clustering utility and fairness, while incurring prohibitive quadratic complexity on large-scale datasets. To address these challenges, we propose Causal Disentangled Anchor Learning (CDAL), a novel framewor…

Cited by 0 Source ↗Scholar ↗