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Conformal Prediction

Conformal Prediction is a machine learning framework that provides valid measures of confidence for individual predictions. It offers a principled approach to quantify uncertainty in predictions without assuming any specific distribution for the data. This section features papers that explore various aspects of conformal prediction, including theoretical advancements, algorithmic developments, and applications across different domains.

Papers

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Showing 221–230 of 704 papers

TitleStatusHype
A Physics-Informed Machine Learning Framework for Safe and Optimal Control of Autonomous Systems—0
The Relationship between No-Regret Learning and Online Conformal Prediction—0
Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreakCode0
Relational Conformal Prediction for Correlated Time Series—0
Image Super-Resolution with Guarantees via Conformalized Generative Models—0
Partial-Label Learning with Conformal Candidate Cleaning—0
On Training-Conditional Conformal Prediction and Binomial Proportion Confidence Intervals—0
Beyond Confidence: Adaptive Abstention in Dual-Threshold Conformal Prediction for Autonomous System PerceptionCode0
Conformal Prediction Regions are Imprecise Highest Density Regions—0
Epistemic Uncertainty in Conformal Scores: A Unified ApproachCode0
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