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Found 3 papers, 1 papers with code
Calibration by Distribution Matching: Trainable Kernel Calibration Metrics
Calibration ensures that probabilistic forecasts meaningfully capture uncertainty by requiring that predicted probabilities align with empirical frequencies.
Modular Conformal Calibration
We introduce a versatile class of algorithms for recalibration in regression that we call Modular Conformal Calibration (MCC).
Online Calibrated and Conformal Prediction Improves Bayesian Optimization
Accurate uncertainty estimates are important in sequential model-based decision-making tasks such as Bayesian optimization.
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