Regression Prediction Intervals

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Regression prediction intervals estimate a range for an outcome, helping people judge the risk of a prediction rather than relying on a point estimate alone. Common approaches include quantile regression, Bayesian models and ensembles, and conformal prediction, which calibrates errors to provide finite-sample marginal coverage under exchangeability. Current work adapts intervals to local difficulty, skew, time dependence, and distribution shift, across settings such as tabular regression, medical prediction, and time-series forecasting. A central tension is that nominal overall coverage does not ensure reliable coverage for particular subgroups or changing conditions, while narrower intervals can come at the cost of undercoverage.

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102 papers16 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

Uncertainty-Aware Solar Flare Regression
Mar 5, 2026Jinsu Hong, Chetraj Pandey, Berkay AydinRegression Prediction Intervals Conformal Prediction
Computer Science, Georgia State University, Atlanta, USA · Computer Science, Texas Christian University, Texas, USA
Co-optimization for Adaptive Conformal Prediction
Mar 2, 2026Xiaoyi Su, Zhixin Zhou, Rui LuoConformal Prediction Regression Prediction Intervals
Department of Systems Engineering, City University of Hong Kong, Hong Kong SAR, China · Alpha Benito Research, Los Angeles, USA
LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees
Feb 25, 2026Vagner Santos, Victor Coscrato, Luben Cabezas +2Conformal Prediction Regression Prediction Intervals
Department of Statistics, Federal University of São Carlos, São Carlos, São Paulo, Brazil · Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, São Paulo, Brazil · Université Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, France
Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features
Feb 22, 2026Michele Caprio, Katerina Papagiannouli, Siu Lun Chau +1Random Feature Methods Bayesian Inference
The University of Manchester, UK · University of Pisa, Italy · Nanyang Technological University, Singapore +1
Geometry-Aware Uncertainty Quantification via Conformal Prediction on Manifolds
Feb 17, 2026Marzieh Amiri Shahbazi, Ali BaheriUncertainty Quantification Conformal Prediction
Rochester Institute of Technology, Rochester, NY, USA.
Calibrated uncertainty quantification for prosumer flexibility aggregation in ancillary service markets
Jan 21, 2026Yogesh Pipada Sunil Kumar, S. Ali Pourmousavi, Jon A. R. Liisberg +1Uncertainty Quantification Uncertainty Calibration
School of Electrical and Mechanical Engineering, The University of Adelaide, Ingkarni Wardli Building, Adelaide, SA 5005, Australia · Watts A/S, Brogade 19D, Køge 4600, Denmark
Online conformal inference with retrospective adjustment for faster adaptation to distribution shift
Nov 6, 2025Jungbin Jun, Ilsang OhnDistribution Shift Robustness Conformal Prediction
Department of Statistics, Inha University
Gradient Boosted Mixed Models: Flexible Estimation of Mean and Variance Components for Clustered Data
Oct 31, 2025Mitchell L. Prevett, Francis K. C. Hui, Zhi Yang Tho +2Gradient-Boosted Decision Trees Heteroscedastic Regression
Research School of Finance, Actuarial Studies and Statistics The Australian National University Canberra, ACT 2601, Australia
Efficient Conformal Prediction for Regression Models under Label Noise
Sep 18, 2025Yahav Cohen, Jacob Goldberger, Tom TirerConformal Prediction Learning with Noisy Labels
Faculty of Engineering Bar-Ilan University Ramat Gan, Israel
Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
Jul 21, 2025Gábor Lugosi, Marcos MatabuenaConformal Prediction k-Nearest Neighbors
Department of Economics and Business, Pompeu Fabra University, Barcelona, Spain · ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Spain · Barcelona Graduate School of Economics +3
Conformalized Regression for Continuous Bounded Outcomes
Jul 18, 2025Zhanli Wu, Fabrizio Leisen, F. Javier RubioConformal Prediction Regression Prediction Intervals
Department of Mathematics King’s College London London, UK · Department of Statistical Science University College London London, UK
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
Jul 10, 2025Ilia Azizi, Juraj Bodik, Jakob Heiss +1Uncertainty Quantification Aleatoric Uncertainty
Department of Operations, HEC, University of Lausanne, Switzerland · BegooAI, Switzerland · Department of Statistics, University of California, Berkeley, USA +1

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Uncertainty-Aware Solar Flare Regression

Jinsu Hong, Chetraj Pandey, Berkay Aydin

Computer Science, Georgia State University, Atlanta, USA · Computer Science, Texas Christian University, Texas, USA

Regression Prediction Intervals Conformal Prediction

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Current solar flare predictions often lack precise quantification of their reliability, resulting in frequent false alarms, particularly when dealing with datasets skewed towards extreme events. To improve the trustworthiness of space weather forecasting, it is crucial to establish confidence intervals for model predictions. Conformal prediction, a machine learning framework, presents a promising avenue for this purpose by constructing prediction intervals that ensure valid coverage in finite samples without making assumptions about the underlying data distribution. In this study, we explore the application of conformal prediction to regression tasks in space weather forecasting. Specifically, we implement full-disk solar flare prediction using images created from magnetic field maps and adapt four pre-trained deep learning models to incorporate three distinct methods for constructing confidence intervals: conformal prediction, quantile regression, and conformalized quantile regression. Our experiments demonstrate that conformalized quantile regression achieves higher coverage rates and more favorable average interval lengths compared to alternative methods, underscoring its effectiveness in enhancing the reliability of solar weather forecasting models.