Prediction Intervals
94 papers with code • 0 benchmarks • 2 datasets
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.
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Friedman1
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Latest papers with no code
Enhancing Interval Type-2 Fuzzy Logic Systems: Learning for Precision and Prediction Intervals
In this context, we first provide extra design flexibility to the Karnik-Mendel (KM) and Nie-Tan (NT) center of sets calculation methods to increase their flexibility for generating PIs.
Zadeh's Type-2 Fuzzy Logic Systems: Precision and High-Quality Prediction Intervals
After detailing the construction of Z-GT2-FLSs, we provide solutions to challenges while learning from high-dimensional data: the curse of dimensionality, and integrating Deep Learning (DL) optimizers.
Enhancing Conformal Prediction Using E-Test Statistics
Conformal Prediction (CP) serves as a robust framework that quantifies uncertainty in predictions made by Machine Learning (ML) models.
Selecting informative conformal prediction sets with false coverage rate control
We consider here the case where such prediction sets come after a selection process.
CAP: A General Algorithm for Online Selective Conformal Prediction with FCR Control
To avoid devoting resources to unimportant units, a preliminary selection of the current individual before reporting its prediction interval is common and meaningful in online predictive tasks.
Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent
We analyze the statistical properties of generalized cross-validation (GCV) and leave-one-out cross-validation (LOOCV) applied to early-stopped gradient descent (GD) in high-dimensional least squares regression.
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks
In this paper, we adopt conformal prediction, a distribution-free uncertainty quantification (UQ) framework, to obtain confidence prediction intervals with coverage guarantees for Deep Operator Network (DeepONet) regression.
Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests
Deep learning models are being adopted and applied on various critical decision-making tasks, yet they are trained to provide point predictions without providing degrees of confidence.
Self-Consistent Conformal Prediction
Conformal prediction helps decision-makers quantify uncertainty in point predictions of outcomes, allowing for better risk management for actions.
A comprehensive framework for multi-fidelity surrogate modeling with noisy data: a gray-box perspective
Gray-box modeling is concerned with the problem of merging information from data-driven (a. k. a.
