aiqbench analysis
This research advances conformal inference by developing noise-adaptive methods that maintain rigorous coverage guarantees despite label imperfections common in large-scale datasets. By addressing the exchangeability assumption violation caused by random noise, the work offers a more robust statistical framework for uncertainty quantification in practical machine learning applications. The findings validate the approach across synthetic benchmarks and real-world image classification tasks like BigEarthNet and CIFAR-10H.
Noise-Adaptive Conformal Classification with Marginal Coverage
arXiv:2501.18060v2 Announce Type: replace-cross Abstract: Conformal inference provides a rigorous statistical framework for uncertainty quantification in machine learning, enabling well-calibrated prediction sets with precise coverage guarantees for any classification model. However, its reliance on the idealized assumption of perfect data exchangeability limits its effectiveness in the presence of real-world complications, such as low-quality labels---a widespread issue in modern large-scale data sets. This work tackles this open problem by introducing an adaptive conformal inference method capable of efficiently handling deviations from exchangeability caused by random label noise, leading to informative prediction sets with tight marginal coverage guarantees even in those challenging scenarios. We validate our method through extensive numerical experiments demonstrating its effectiveness on synthetic and real data sets, including BigEarthNet and CIFAR-10H.
Abstract:Conformal inference provides a rigorous statistical framework for uncertainty quantification in machine learning, enabling well-calibrated prediction sets with precise coverage guarantees for any classification model. However, its reliance on the idealized assumption of perfect data exchangeability limits its effectiveness in the presence of real-world complications, such as low-quality labels---a widespread issue in modern large-scale data sets. This work tackles this open problem by introducing an adaptive conformal inference method capable of efficiently handling deviations from exchangeability caused by random label noise, leading to informative prediction sets with tight marginal coverage guarantees even in those challenging scenarios. We validate our method through extensive numerical experiments demonstrating its effectiveness on synthetic and real data sets, including BigEarthNet and CIFAR-10H.
Methodology (stat.ME); Machine Learning (cs.LG); Machine Learning (stat.ML)
arXiv:2501.18060 [stat.ME]
(or arXiv:2501.18060v2 [stat.ME] for this version)
https://doi.org/10.48550/arXiv.2501.18060 Focus to learn more arXiv-issued DOI via DataCite
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