A method of supervised learning from conflicting data with hidden contexts

• URL: http://arxiv.org/abs/2108.12113v3
• Date: Thu, 13 Feb 2025 13:12:41 GMT
• Title: A method of supervised learning from conflicting data with hidden contexts
• Authors: Tianren Zhang, Yizhou Jiang, Feng Chen,
• Abstract summary: We formulate a more general supervised learning problem in which training data is drawn from multiple unobservable domains. This inherent conflict in data renders standard empirical risk minimization training ineffective. We propose a method LEAF that introduces an allocation function, which learns to assign conflicting data to different predictive models.
• Score: 6.459225559630302
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• Abstract: Conventional supervised learning assumes a stable input-output relationship. However, this assumption fails in open-ended training settings where the input-output relationship depends on hidden contexts. In this work, we formulate a more general supervised learning problem in which training data is drawn from multiple unobservable domains, each potentially exhibiting distinct input-output maps. This inherent conflict in data renders standard empirical risk minimization training ineffective. To address this challenge, we propose a method LEAF that introduces an allocation function, which learns to assign conflicting data to different predictive models. We establish a connection between LEAF and a variant of the Expectation-Maximization algorithm, allowing us to derive an analytical expression for the allocation function. Finally, we provide a theoretical analysis of LEAF and empirically validate its effectiveness on both synthetic and real-world tasks involving conflicting data.

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