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arXiv Machine Learning By Liangkai Zhou, Susu Xu, Shuqi Zhong, Shan Lin

Multi-Task Anti-Causal Learning for Reconstructing Urban Events from Residents' Reports

Read the original on arXiv Machine Learning →

The paper introduces Multi-Task Anti-Causal learning (MTAC), a framework that estimates latent causes from observed effects by exploiting both task-invariant and task-specific structural dependencies. MTAC constructs a structural equation model that separates a shared backbone mechanism from task-specific deviations, then uses maximum a posteriori inference to reconstruct causes. Applied to urban event reconstruction—parking violations, abandoned properties, and unsanitary conditions—MTAC outperforms strong baselines on real data from Manhattan and Newark, achieving up to a 33.04% reduction in mean absolute error.

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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