Guanlin Mo — accepted AI-conference papers

2026

Sample-and-Search: An Effective Algorithm for Learning-Augmented k-Median Clustering in High Dimensions

Kangke Cheng, Shihong Song, Guanlin Mo, Hu Ding

AAAI 2026technical

In this paper, we investigate the learning-augmented k-median clustering problem, which aims to improve the performance of traditional clustering algorithms by preprocessing the point set with a predictor of error rate α ∈ [0,1). This preprocessing step assigns potential labels to the points before

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2025

Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems

Shihong Song, Guanlin Mo, Hu Ding

ICLR 2025poster

The fairness of clustering algorithms has gained widespread attention across various areas, including machine learning, In this paper, we study fair $k$-means clustering in Euclidean space. Given a dataset comprising several groups, the fairness constraint requires that each cluster should contai…

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