AINeutralarXiv – CS AI · 3h ago6/10
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Adapting, Fast and Slow: On Few-Shot Transportability of Compositions
Researchers present a framework for cross-domain generalization in machine learning that extends causal transportability theory to handle sequential prediction tasks. The work introduces module and circuit transportability, enabling models to compose learned mechanisms from source domains to make zero-shot predictions on target domains, with practical few-shot learning methods requiring minimal target domain data.