Human-Machine Collaboration on Generative Meta-Learning: Model and Algorithm

Generalizing machine learning models to environments that differ from their training distribution remains a critical hurdle, particularly when data from the target domain is entirely or partially unavailable. We propose Generative Meta-Learning with Human Feedback (GMHF), a novel framework that bridges this domain gap by leveraging expert intuition to guide data synthesis. Grounded in a theoretical analysis of generalization error, we derive bounds demonstrating that aligning the distribution of

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ethics.ai (1 July 2026), “Human-Machine Collaboration on Generative Meta-Learning: Model and Algorithm,” evidence record 353, https://ethics.ai/record/353 (originally published by arXiv).

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