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 o
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- PossiblePossibly related (embedding) · 48%ai-collection/ai-collection →
- PossiblePossibly related (embedding) · 45%Anti-Causal Domain Generalization: Leveraging Unlabeled Data - Apple Machine Learning Research →
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“Fuzzy title match (0.92): “Human-Machine Collaboration on Generative Meta-Learning: Mod” ≈ “GoogleCloudPlatform/generative-ai””
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- FuzzySimilar title/name (fuzzy) · 66%DataTalksClub/machine-learning-zoomcamp →
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- FuzzySimilar title/name (fuzzy) · 59%EthicalML/awesome-production-machine-learning →
“Fuzzy title match (0.73): “Human-Machine Collaboration on Generative Meta-Learning: Mod” ≈ “EthicalML/awesome-production-machine-learning””
