RDDG is an in-context learning system with dynamic guidance and automatic quality feedback that synthesizes high-fidelity relational data to improve imbalanced classification.
InAdvances in Neural Infor- mation Processing Systems (NeurIPS 2024), pages 45155–45205
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Self-Reinforcing Controllable Synthesis of Rare Relational Data via Bayesian Calibration
RDDG is an in-context learning system with dynamic guidance and automatic quality feedback that synthesizes high-fidelity relational data to improve imbalanced classification.