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arXiv Machine Learning By Chendi Qian, Christopher Morris, Stefanie Jegelka, Christian Sohler

Learning to Approximate Uniform Facility Location via Graph Neural Networks

Read the original on arXiv Machine Learning →

The paper introduces a fully differentiable message‑passing neural network (MPNN) designed to approximate the Uniform Facility Location (UniFL) problem. Unlike many learning‑based approaches that require supervision or reinforcement learning, this model incorporates principles from classical approximation algorithms, providing provable approximation guarantees. Empirical results show that it outperforms standard approximation algorithms and reduces the performance gap to integer linear programming solutions.

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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