New research advances conformal prediction for uncertainty quantification · 8 sources tracked
ByPulseAugur Editorial·[10 sources]·
Researchers have developed new theoretical frameworks and computational methods to enhance conformal prediction, a technique for quantifying uncertainty in machine learning models. One paper proposes an optimal data splitting strategy for split conformal prediction to minimize prediction interval lengths while maintaining coverage guarantees, applicable to various regression settings including neural networks. Another line of research introduces approximate leave-one-out estimators to accelerate conformal prediction, achieving comparable coverage and efficiency to exact methods with significantly reduced runtime. Additionally, new approaches are being explored for macro-coverage guarantees in classification tasks, particularly for long-tailed datasets, and for addressing stochasticity in full conformal prediction.
AI
IMPACT
Advances in conformal prediction can lead to more reliable uncertainty quantification in AI models, improving trust and safety in critical applications.
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Multiple academic papers published on arXiv detailing theoretical and methodological advancements in conformal prediction.
arXiv:2606.31600v1 Announce Type: cross Abstract: Conformal prediction and its variants, including the split conformal prediction, provide a distribution-free framework for uncertainty quantification by constructing prediction intervals or sets with finite-sample coverage guarant…
arXiv:2606.31915v1 Announce Type: cross Abstract: While conformal prediction provides a general framework for uncertainty quantification in predictive inference, its application is often limited by computational cost. Recent methods, including Jackknife+ and Jackknife-minmax, ach…
While conformal prediction provides a general framework for uncertainty quantification in predictive inference, its application is often limited by computational cost. Recent methods, including Jackknife+ and Jackknife-minmax, achieve faster computation by trading a slight loss o…
arXiv cs.LG
TIER_1English(EN)·Soumendra N. Lahiri·
Conformal prediction and its variants, including the split conformal prediction, provide a distribution-free framework for uncertainty quantification by constructing prediction intervals or sets with finite-sample coverage guarantees. The statistical efficiency of these intervals…
Conformal prediction guarantees marginal coverage, but pooled calibration averages over heterogeneous regions and can mask regional undercoverage in safety-critical subgroups. We introduce Self-Organized Conformal Prediction (SOCP), a calibration scheme that discovers input-space…
arXiv stat.ML
TIER_1English(EN)·Haifeng Wen, Osvaldo Simeone, Hong Xing·
arXiv:2603.14198v3 Announce Type: replace-cross Abstract: Deploying trustworthy AI systems requires principled uncertainty quantification. Conformal prediction (CP) is a widely used framework for constructing prediction sets with distribution-free coverage guarantees. In many pra…
arXiv:2606.28730v1 Announce Type: cross Abstract: The theory of full conformal prediction uses deterministic non-conformity measure, but modern usage of full conformal prediction often relies on machine learning training, making stochasticity inevitable. A simple sufficient condi…
arXiv:2606.28598v1 Announce Type: cross Abstract: Prediction sets should have high coverage to be useful, but some coverage notions are more practically relevant than others. In the classification setting, class-conditional coverage requires that the prediction set (i.e., the set…
The theory of full conformal prediction uses deterministic non-conformity measure, but modern usage of full conformal prediction often relies on machine learning training, making stochasticity inevitable. A simple sufficient condition of almost sure permutation invariance of the …
Prediction sets should have high coverage to be useful, but some coverage notions are more practically relevant than others. In the classification setting, class-conditional coverage requires that the prediction set (i.e., the set of candidate labels for a new test point) must ac…
