View source for Out-of-Distribution (OOD) Generalization

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* [[In-Context Learning (ICL)]] ... [[Context]] ... [[Causation vs. Correlation]] ... [[Autocorrelation]] ... [[Out-of-Distribution (OOD) Generalization]] ... [[Transfer Learning]]
* [[Singularity]] ... [[Artificial Consciousness / Sentience|Sentience]] ... [[Artificial General Intelligence (AGI)| AGI]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ...  [[Algorithm Administration#Automated Learning|Automated Learning]]
* [[Math for Intelligence#Mathematical Reasoning|Mathematical Reasoning]]
* [https://arxiv.org/abs/2108.13624 Towards Out-Of-Distribution Generalization: A Survey | Z. Shen, J. Liu, Y. He, X. Zhang, R. Xu, H. Yu, P. Cui - arXiv - Cornell University]
* [https://arxiv.org/abs/2106.04496 Towards a Theoretical Framework of Out-of-Distribution Generalization | H. Ye, C. Xie, T. Cai, R. Li, Z. Li, L. Wang - arXiv - Cornell University]
* [http://proceedings.mlr.press/v139/krueger21a/krueger21a.pdf Out-of-Distribution Generalization via Risk Extrapolation | D. Krueger, E. Caballero, J. Jacobsen, A. Zhang, Jonathan Binas, D. Zhang, R. Le Priol, A. Courville]
* [https://arxiv.org/abs/2210.10636 Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Recommendation Systems | P. Bansal, Y. Prabhu, E. Kiciman, A. Sharma]
* [https://link.springer.com/chapter/10.1007/978-3-030-92659-5_39 How Reliable Are Out-of-Distribution Generalization Methods for Medical Image Segmentation? | A. Sanner, C. González & A. Mukhopadhyay]
* [https://link.springer.com/chapter/10.1007/978-3-031-25075-0_36 Meta-Causal Feature Learning for Out-of-Distribution Generalization | Y. Wang, X. Li, Z. Qi, J. Li, X. Li, X. Meng & L. Meng]


Out-of-Distribution (OOD) generalization refers to the ability of a machine learning model to generalize to new data that comes from a different distribution than the training data. This is a challenging problem because the testing distribution is unknown and different from the training distribution. There are several methods for improving out-of-distribution generalization. According to a survey on the topic, existing methods can be categorized into three parts based on their positions in the whole learning pipeline: unsupervised representation learning, supervised model learning and optimization. Another approach to out-of-distribution generalization is via learning domain-invariant features or hypothesis-invariant features.

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= Difference Between In-Context Learning and OOD Generalization =
* [[In-Context Learning (ICL)]] ... [[Context]]

[[In-Context Learning (ICL)]] refers to the ability of a machine learning model to learn from a few examples provided in the context of a task, without any fine-tuning. This is also known as few-shot learning or zero-shot learning.

Out-of-distribution (OOD) generalization, on the other hand, refers to the ability of a machine learning model to generalize to new data that comes from a different distribution than the training data1.

The main difference between [[In-Context Learning (ICL)]] and OOD generalization is that [[In-Context Learning (ICL)|in-context learning]] focuses on the ability of a model to learn from a few examples provided in the context of a task, while OOD generalization focuses on the ability of a model to generalize to new data that comes from a different distribution than the training data.