PFWCP achieves personalized asymptotic marginal and calibration-conditional coverage in federated conformal prediction via density ratio weighting and quantile aggregation under one-shot communication.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Low-resolution data improves high-resolution model performance when high-resolution samples are limited, via KL-divergence bounds and experiments on vision transformers and CNNs.
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Multi-Agent Conformal Prediction with Personalized Statistical Validity
PFWCP achieves personalized asymptotic marginal and calibration-conditional coverage in federated conformal prediction via density ratio weighting and quantile aggregation under one-shot communication.
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On What We Can Learn from Low-Resolution Data
Low-resolution data improves high-resolution model performance when high-resolution samples are limited, via KL-divergence bounds and experiments on vision transformers and CNNs.