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A Method of Supervised Learning from Conflicting Data with Hidden Contexts by Zhang, Tianren; Jiang, Yizhou; Chen, Feng is a scholarly article available to read on EtoBox.

What is A Method of Supervised Learning from Conflicting Data with Hidden Contexts about?

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.

Author

Zhang, Tianren; Jiang, Yizhou; Chen, Feng

Published

2021

Language

EN