An Out-of-Distribution Generalization Framework Based on Variational Backdoor Adjustment

An Out-of-Distribution Generalization Framework Based on Variational Backdoor Adjustment

In practical applications, learning models that can perform well even when the data distribution is different from the training set are essential and meaningful. Such problems are often referred to as out-of-distribution (OOD) generalization problems. In this paper, we propose a method for OOD gener...

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Bibliographic Details
Main Authors: Hang Su, Wei Wang
Format: Article
Language:English
Published: MDPI AG 2023-12-01
Series:Mathematics
Subjects:
out-of-distribution generalization
causal inference
machine learning
backdoor adjustment
supervised learning
Online Access:https://www.mdpi.com/2227-7390/12/1/85

Internet

https://www.mdpi.com/2227-7390/12/1/85

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