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Prediction Intervals

A prediction interval is an estimate of an interval in which a future observation will fall, with a certain probability, given what has already been observed. Prediction intervals are often used in regression analysis.

Papers

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Showing 131–140 of 309 papers

TitleStatusHype
Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning—0
A Minimax-MDP Framework with Future-imposed Conditions for Learning-augmented Problems—0
Gaussian process interpolation with conformal prediction: methods and comparative analysis—0
How to Evaluate Uncertainty Estimates in Machine Learning for Regression?—0
Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback—0
Foundation models for time series forecasting: Application in conformal prediction—0
From Conformal Predictions to Confidence Regions—0
Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent—0
Extreme Conformal Prediction: Reliable Intervals for High-Impact Events—0
A Wireless Foundation Model for Multi-Task Prediction—0
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