PCS-UQ: New Framework for Uncertainty Quantification in Machine Learning
Researchers have introduced PCS-UQ, a framework for uncertainty quantification in machine learning that integrates predictability, computability, and stability principles to produce more reliable prediction intervals. The method builds on conformal prediction techniques, adding a novel multiplicative calibration scheme and bootstrap sampling to capture both data variability and algorithmic instability. Benchmarked across 17 regression and 6 classification datasets, PCS-UQ reduces prediction set sizes by up to 20% while maintaining consistent coverage across subgroups, which is critical for safe deployment in high-stakes domains.
PCS-UQ is a new uncertainty quantification framework proposed by researchers and posted to arXiv, grounded in the Predictability, Computability, and Stability (PCS) principles developed for veridical data science. The framework begins with a candidate set of models, applies a prediction-check to screen out unsuitable algorithms, and uses bootstrap sampling to account for both inter-sample variability and algorithmic instability. A novel multiplicative calibration scheme is introduced to improve local adaptivity, effectively functioning as a new score within the conformal prediction paradigm. On a benchmark of 17 real-world regression datasets with manually constructed subgroups, PCS-UQ maintained target coverage while matching or outperforming conformal methods that used oracle-selected algorithms, and achieved more consistent subgroup coverage. On six classification datasets, prediction set sizes were reduced by 20%, with similar gains on three computer vision benchmarks using computationally efficient deep learning variants that avoid expensive retraining. The authors also provide a theoretical proof that a modified version of PCS-UQ preserves valid coverage under exchangeability, situating it formally within the split conformal inference literature.
What's missing
The theoretical coverage guarantee applies to a modified version of the algorithm rather than the primary proposed method, and the gap between the two is not fully characterized. The paper has not yet undergone peer review.
What different sources said
- arXiv cs.LGCenter
PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework
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