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 generated data with human beliefs regarding the target physics significantly mitigates risk. GMHF operationalizes this insight by employing a Conditional Neural ODE (cNODE) as a generative digital...
| Authors | Midhun Parakkal Unni, Samuel Kaski |
|---|---|
| Primary category | cs.LG |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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