Synthetic minority augmentation improves threshold-integrated and optimized classification metrics only under model misspecification by correcting ranking errors, while providing no fundamental benefit beyond possible variance reduction under well-specified score models.
arXiv preprint arXiv:2503.21968 , year=
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Proposes task exchangeability as a condition for valid inference when using synthetic data in scientific research, with methods and extensions demonstrated on surveys and AI evaluations.
This review synthesizes representative advances in high-dimensional statistics, highlights common themes and open problems, and points to key entry works.
citing papers explorer
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When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification?
Synthetic minority augmentation improves threshold-integrated and optimized classification metrics only under model misspecification by correcting ranking errors, while providing no fundamental benefit beyond possible variance reduction under well-specified score models.
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Valid Inference with Synthetic Data via Task Exchangeability
Proposes task exchangeability as a condition for valid inference when using synthetic data in scientific research, with methods and extensions demonstrated on surveys and AI evaluations.
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High-Dimensional Statistics: Reflections on Progress and Open Problems
This review synthesizes representative advances in high-dimensional statistics, highlights common themes and open problems, and points to key entry works.