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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 276–300 of 309 papers

TitleStatusHype
Conditional validity of heteroskedastic conformal regressionCode0
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled ApproachCode0
Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement LearningCode0
Conformal prediction for frequency-severity modelingCode0
Optimal Aggregation of Prediction Intervals under Unsupervised Domain ShiftCode0
Conformal Prediction for Dose-Response Models with Continuous TreatmentsCode0
Conformal Prediction for Causal Effects of Continuous TreatmentsCode0
Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction SetsCode0
Uncertainty-Aware Boosted Ensembling in Multi-Modal SettingsCode0
Probabilistic AutoRegressive Neural Networks for Accurate Long-range ForecastingCode0
Inference on Extreme Quantiles of Unobserved Individual HeterogeneityCode0
Inference on the Sharpe ratio via the upsilon distributionCode0
Informativeness of Weighted Conformal PredictionCode0
Integrating Uncertainty Awareness into Conformalized Quantile RegressionCode0
RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted ForestsCode0
Adapting GT2-FLS for Uncertainty Quantification: A Blueprint Calibration StrategyCode0
Subgroup-Specific Risk-Controlled Dose Estimation in RadiotherapyCode0
Perturbation-Assisted Sample Synthesis: A Novel Approach for Uncertainty QuantificationCode0
Conformalized Deep Splines for Optimal and Efficient Prediction SetsCode0
Confident Neural Network Regression with Bootstrapped Deep EnsemblesCode0
Target Strangeness: A Novel Conformal Prediction Difficulty EstimatorCode0
Task-Driven Uncertainty Quantification in Inverse Problems via Conformal PredictionCode0
Test-time Recalibration of Conformal Predictors Under Distribution Shift Based on Unlabeled ExamplesCode0
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical ModelsCode0
A Unified Framework for Random Forest Prediction Error EstimationCode0
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