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
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.
Reliable Prediction Intervals with Regression Neural Networks
This paper proposes an extension to conventional regression Neural Networks (NNs) for replacing the point predictions they produce with prediction intervals that satisfy a required level of confidence.
Sequential inductive prediction intervals
In this paper we explore the concept of sequential inductive prediction intervals using theory from sequential testing.
Adaptability of Computer Vision at the Tactical Edge: Addressing Environmental Uncertainty
By incorporating UQ into the core operations surrounding C2 and CV systems at the tactical edge, we can help drive purposeful adaptability on the battlefield.
Stability of Random Forests and Coverage of Random-Forest Prediction Intervals
With another mild condition that is typically satisfied when $Y$ is continuous, we also establish a complementary upper bound, which can be similarly established for the jackknife prediction interval constructed from an arbitrary stable algorithm.
Guaranteed Coverage Prediction Intervals with Gaussian Process Regression
Gaussian Process Regression (GPR) is a popular regression method, which unlike most Machine Learning techniques, provides estimates of uncertainty for its predictions.
Approaches for Uncertainty Quantification of AI-predicted Material Properties: A Comparison
The development of large databases of material properties, together with the availability of powerful computers, has allowed machine learning (ML) modeling to become a widely used tool for predicting material performances.
The WayHome: Long-term Motion Prediction on Dynamically Scaled
One of the key challenges for autonomous vehicles is the ability to accurately predict the motion of other objects in the surrounding environment, such as pedestrians or other vehicles.
Distribution-free risk assessment of regression-based machine learning algorithms
We solve the risk-assessment problem using the conformal prediction approach, which provides prediction intervals that are guaranteed to contain the true label with a given probability.
