Generalized conformal predictive systems are extended to non-exchangeable settings under distributional shifts via permutation weights and robust weight-uncertainty boxes with finite-sample or asymptotic guarantees.
Ziegel, and Tilmann Gneiting
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Online conformal prediction post-processing guarantees calibrated uncertainty coverage for GenCast, NeuralGCM, and AIFS-ENS forecasts of temperature and precipitation including extremes.
Predictive Bayesian inference posteriors concentrate onto a forward-model-dependent quantity and produce miscalibrated credible sets unless the predictive model contains the true data-generating process.
EnScale emulates high-resolution regional climate model outputs from global circulation models for multiple variables using a two-step generative process with sparse local stochastic layers and energy score optimization, including a temporally consistent variant.
Presents a robust algorithm for learning any coordinate-wise non-decreasing evaluator preference function, with theoretical guarantees that it matches linear performance when linearity holds.
citing papers explorer
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Generalized Conformal Predictive Systems Under Distributional Shifts
Generalized conformal predictive systems are extended to non-exchangeable settings under distributional shifts via permutation weights and robust weight-uncertainty boxes with finite-sample or asymptotic guarantees.
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Rigorous uncertainty quantification of probabilistic AI weather forecasts with conformal prediction
Online conformal prediction post-processing guarantees calibrated uncertainty coverage for GenCast, NeuralGCM, and AIFS-ENS forecasts of temperature and precipitation including extremes.
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Concentration and Calibration in Predictive Bayesian Inference
Predictive Bayesian inference posteriors concentrate onto a forward-model-dependent quantity and produce miscalibrated credible sets unless the predictive model contains the true data-generating process.
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EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules
EnScale emulates high-resolution regional climate model outputs from global circulation models for multiple variables using a two-step generative process with sparse local stochastic layers and energy score optimization, including a temporally consistent variant.
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Learning What Evaluators Value: A Reliable Approach to Modeling Evaluator Preferences
Presents a robust algorithm for learning any coordinate-wise non-decreasing evaluator preference function, with theoretical guarantees that it matches linear performance when linearity holds.