Zeyu Lin

2 accepted papers

2026

Improved Streaming Algorithm for Fair k-Center Clustering

Longkun Guo, Zeyu Lin, Chaoqi Jia, Chao Chen

AAAI 2026technical

Many real-world applications call for incorporating fairness constraints into the k-center clustering problem, where the dataset is partitioned into m demographic groups, each with a specified upper bound on the number of centers to ensure fairness. Focusing on big data scenarios, this paper address

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2026

Parameterized and Streaming Algorithms for Euclidean Fair k-Center Clustering

Zeyu Lin, Chaoqi Jia, Longkun Guo, Chao Chen

IJCAI 2026

Motivated by the growing importance of fairness in machine learning, fair $k$-center clustering has attracted considerable research attention as a fundamental problem. In this problem, a dataset is partitioned into $m$ disjoint groups, and the objective is to select $k$ data points as centers, subje

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