Meta-Learning Causal Feature Selection for Stable Prediction

Yuan, Zhaoquan; Xiao, Peng; Wu, Xiao; Xu, Changsheng

doi:10.1109/icme51207.2021.9428205

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…[4], [12], [13], [118], [119], [120], [121], [122], [123], [124](Section 4.3) Optimization Methods (Section 5) Distributionally Robust Optimization (Section 5.1) [1], [125], [126], [127], [128], [129], [130], [131], [132], [133], [134](Section 5.1.1∼ 5.1.3) [135], [136], [137], [138] (Section 5.1.4) Invariance-Based Optimization (Section 5.2) [5], [139], [140](Section 5.2) In real scenarios where observations are made in the form of images or sentences instead of structured data, high-level abstract information needs to be extracted from low-level data [27], and a few existing works [34], [35], [36] propose to recover causal factorization through disentanglement.…”

Section: Causal Representation Learningmentioning

confidence: 99%

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Towards Out-Of-Distribution Generalization: A Survey

Shen

Liu²,

et al. 2021

Preprint

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Classic machine learning methods are built on the i.i.d. assumption that training and testing data are independent and identically distributed. However, in real scenarios, the i.i.d. assumption can hardly be satisfied, rendering the sharp drop of classic machine learning algorithms' performances under distributional shifts, which indicates the significance of investigating the Out-of-Distribution generalization problem. Out-of-Distribution (OOD) generalization problem addresses the challenging setting where the testing distribution is unknown and different from the training. This paper serves as the first effort to systematically and comprehensively discuss the OOD generalization problem, from the definition, methodology, evaluation to the implications and future directions. Firstly, we provide the formal definition of the OOD generalization problem. Secondly, existing methods are categorized into three parts based on their positions in the whole learning pipeline, namely unsupervised representation learning, supervised model learning and optimization, and typical methods for each category are discussed in detail. We then demonstrate the theoretical connections of different categories, and introduce the commonly used datasets and evaluation metrics. Finally, we summarize the whole literature and raise some future directions for OOD generalization problem. The summary of OOD generalization methods reviewed in this survey can be found at http://out-of-distribution-generalization.com.

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Section: Causal Representation Learningmentioning

confidence: 99%

“…Zhang et al [122] propose a Deconfounded Visio-Linguistic Bert framework to mitigate the potential data biases. And Yuan et al [123] propose to identify causal features with meta-learning mechanism for OOD generalization.…”

Section: Stable Learningmentioning

confidence: 99%

Towards Out-Of-Distribution Generalization: A Survey

Shen

Liu²,

et al. 2021

Preprint

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Overview of Universal Behavior Computing

Wang,

Zhu

2024

Universal Behavior Computing for Security and Safety

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A survey of deep causal models and their industrial applications

Li,

Guo,

Qiang

2024

Artif Intell Rev

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The notion of causality assumes a paramount position within the realm of human cognition. Over the past few decades, there has been significant advancement in the domain of causal effect estimation across various disciplines, including but not limited to computer science, medicine, economics, and industrial applications. Given the continous advancements in deep learning methodologies, there has been a notable surge in its utilization for the estimation of causal effects using counterfactual data. Typically, deep causal models map the characteristics of covariates to a representation space and then design various objective functions to estimate counterfactual data unbiasedly. Different from the existing surveys on causal models in machine learning, this review mainly focuses on the overview of the deep causal models based on neural networks, and its core contributions are as follows: (1) we cast insight on a comprehensive overview of deep causal models from both timeline of development and method classification perspectives; (2) we outline some typical applications of causal effect estimation to industry; (3) we also endeavor to present a detailed categorization and analysis on relevant datasets, source codes and experiments.

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Meta-Learning Causal Feature Selection for Stable Prediction

Cited by 4 publications

References 18 publications

Towards Out-Of-Distribution Generalization: A Survey

Towards Out-Of-Distribution Generalization: A Survey

Overview of Universal Behavior Computing

A survey of deep causal models and their industrial applications

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