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arXiv Machine Learning By Siqi Li, Molei Liu, Yiwei Lyu, Ziye Tian, Chuan Hong, Nan Liu

Robust Mixture Models for Algorithmic Fairness Under Latent Heterogeneity

Read the original on arXiv Machine Learning →

The paper introduces ROME, a framework that learns latent group structure while optimizing worst-group predictive performance. ROME links latent-variable modeling with distributionally robust optimization through an Expectation-Maximization approach for linear models and a neural Mixture-of-Experts for nonlinear settings. Experiments on simulations and three real-world regression datasets show that ROME improves worst-group performance while maintaining competitive overall accuracy compared to existing group-aware and group-label-free robust learning methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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