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
aGender
Most implemented papers
Distribution-Free Predictive Inference For Regression
In the spirit of reproducibility, all of our empirical results can also be easily (re)generated using this package.
Conformalized Quantile Regression
Conformal prediction is a technique for constructing prediction intervals that attain valid coverage in finite samples, without making distributional assumptions.
Conformal Inference for Online Prediction with Arbitrary Distribution Shifts
We consider the problem of forming prediction sets in an online setting where the distribution generating the data is allowed to vary over time.
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models
neitzal/adaptive-skip-intervals
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NeurIPS 2018
We introduce a method which enables a recurrent dynamics model to be temporally abstract.
Conformal prediction interval for dynamic time-series
hamrel-cxu/EnbPI
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We develop a method to construct distribution-free prediction intervals for dynamic time-series, called \Verb|EnbPI| that wraps around any bootstrap ensemble estimator to construct sequential prediction intervals.
Conformalized Survival Analysis
Existing survival analysis techniques heavily rely on strong modelling assumptions and are, therefore, prone to model misspecification errors.
RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests
The set of methods implemented in the package includes a new method to build prediction intervals with boosted forests (PIBF) and 15 method variations to produce prediction intervals with random forests, as proposed by Roy and Larocque (2020).
Monitoring Model Deterioration with Explainable Uncertainty Estimation via Non-parametric Bootstrap
In this work, we use non-parametric bootstrapped uncertainty estimates and SHAP values to provide explainable uncertainty estimation as a technique that aims to monitor the deterioration of machine learning models in deployment environments, as well as determine the source of model deterioration when target labels are not available.
Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging
Image-to-image regression is an important learning task, used frequently in biological imaging.
Adaptive Conformal Predictions for Time Series
While recent works tackled this issue, we argue that Adaptive Conformal Inference (ACI, Gibbs and Cand{\`e}s, 2021), developed for distribution-shift time series, is a good procedure for time series with general dependency.
