arXiv AI
Jun 2
Multi-Agent Conformal Prediction with Personalized Statistical Validity
arXiv:2606. 00717v1 Announce Type: cross Abstract: Uncertainty quantification is essential in high-stakes machine learning tasks.
Efficient Federated Conformal Prediction with Group-Conditional Guarantee
arXiv:2603. 14198v3 Announce Type: replace-cross Abstract: Deploying trustworthy AI systems requires principled uncertainty quantification.
Related stories
arXiv Machine Learning
Jun 2
Parameter-Free and Group Conditional Online Conformal Prediction
arXiv:2606. 00419v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) is critical for the deployment of machine learning predictors in real-world scenarios where the data distribution may shift over time (i.
arXiv Machine Learning
Jul 21
Isotonic Conformal Prediction
arXiv:2607. 16675v1 Announce Type: cross Abstract: A point prediction that is well calibrated on average can still be systematically biased conditional on its own value, undermining its use in downstream decision-making.
arXiv Machine Learning
Jun 2
FedCF: Fair Federated Conformal Prediction
arXiv:2509. 22907v2 Announce Type: replace Abstract: Conformal Prediction (CP) is a widely used technique for quantifying uncertainty in machine learning models.
arXiv Machine Learning
Jun 11
PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework
arXiv:2505. 08784v2 Announce Type: replace-cross Abstract: As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety.
arXiv Machine Learning
Jun 10
Optimization-based Online Conformal Prediction for Multi-step Forecasting
arXiv:2508. 13362v2 Announce Type: replace Abstract: Conformal prediction (CP) is well-suited for uncertainty quantification in time series forecasting due to its distribution-free coverage guarantees.
arXiv Machine Learning
Jun 3
Set-Preserving Calibration from Conformal P-Values to E-Values
arXiv:2606. 03600v1 Announce Type: cross Abstract: Standard conformal prediction (CP) procedures are typically formulated in terms of p-values, but reliance on p-values alone limits flexibility, for example, when combining dependent evidence across models or data splits.
arXiv Machine Learning
Jun 16
Audited Conformal Prediction for Classification under Unknown Distribution Shift
arXiv:2606. 14909v1 Announce Type: cross Abstract: We consider the problem of uncertainty quantification for a pretrained classification model deployed under unknown distribution shift.
arXiv Machine Learning
Aug 10
Conformal Fusion Under Missing Modalities
arXiv:2608. 07183v1 Announce Type: new Abstract: Multimodal fusion architectures typically assume all modalities are available at inference, yet sensor failures, acquisition variability, and cost constraints routinely produce incomplete observations.
arXiv Machine Learning
Jul 20
Improving Backward Conformal Prediction via Non-Conformity Score Transformation
arXiv:2602. 01733v3 Announce Type: replace-cross Abstract: Conformal Prediction (CP) provides a statistical framework for uncertainty quantification that constructs prediction sets with coverage guarantees.
arXiv Machine Learning
Jun 30
Conformal Prediction with Macro-Coverage Guarantees
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.
arXiv Machine Learning
Jul 10
Multi-Distribution Robust Conformal Prediction
arXiv:2601. 02998v2 Announce Type: replace Abstract: In many fairness and distribution robustness problems, one has access to labeled data from multiple source distributions yet the test data may come from an arbitrary member or a mixture of them.