What is Gained from Past Learning

Journal of Causal Inference

article

Judea Pearl¹ 

[1] Cognitive Systems Laboratory, Computer Science Department, University of California

关键词: Transfer Leaning;    Domain Adaptation;    Robustness;    Compositional Regression;    Causal Models;   

DOI  :  10.1515/jci-2018-0005

来源: De Gruyter

【 摘 要 】

We consider ways of enabling systems to apply previously learned information to novel situations so as to minimize the need for retraining. We show that theoretical limitations exist on the amount of information that can be transported from previous learning, and that robustness to changing environments depends on a delicate balance between the relations to be learned and the causal structure of the underlying model. We demonstrate by examples how this robustness can be quantified.

【 授权许可】

CC BY