SOCP uses self-organizing maps for unsupervised group discovery to enable local calibration in conformal prediction, reducing regional coverage gaps on benchmarks with small set-size increases while preserving validity guarantees.
Conformalized Quantile Regression
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abstract
Conformal prediction is a technique for constructing prediction intervals that attain valid coverage in finite samples, without making distributional assumptions. Despite this appeal, existing conformal methods can be unnecessarily conservative because they form intervals of constant or weakly varying length across the input space. In this paper we propose a new method that is fully adaptive to heteroscedasticity. It combines conformal prediction with classical quantile regression, inheriting the advantages of both. We establish a theoretical guarantee of valid coverage, supplemented by extensive experiments on popular regression datasets. We compare the efficiency of conformalized quantile regression to other conformal methods, showing that our method tends to produce shorter intervals.
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Robust optimization framework for green ammonia that ensures feasible capacity plans under renewable uncertainty where constraint aggregation fails, using scenario reduction and adaptive policies.
Compares Delta method, Bayesian Monte Carlo Dropout, Bootstrap, Lower-Upper Bound Estimation, and Mean-Variance Estimation for prediction intervals on turbine gas temperature data using coverage probability, normalized mean prediction interval width, and coverage width-based criterion.
citing papers explorer
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Self-Organized Conformal Prediction: Reducing Regional Coverage Gaps with Unsupervised Group Discovery
SOCP uses self-organizing maps for unsupervised group discovery to enable local calibration in conformal prediction, reducing regional coverage gaps on benchmarks with small set-size increases while preserving validity guarantees.
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Robust Optimization for Green Ammonia Production
Robust optimization framework for green ammonia that ensures feasible capacity plans under renewable uncertainty where constraint aggregation fails, using scenario reduction and adaptive policies.
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Benchmarking Machine Learning Uncertainty Quantification Methodologies for Predicting Turbine Gas Temperature Degradation
Compares Delta method, Bayesian Monte Carlo Dropout, Bootstrap, Lower-Upper Bound Estimation, and Mean-Variance Estimation for prediction intervals on turbine gas temperature data using coverage probability, normalized mean prediction interval width, and coverage width-based criterion.