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Methods for reasoning under uncertainty are a key building block of accurateand reliable machine learning systems. Bayesian methods provide a generalframework to quantify uncertainty. However, because of model misspecificationand the use of approximate inference, Bayesian uncertainty estimates are ofteninaccurate -- for example, a 90% credible interval may not contain the trueoutcome 90% of the time. Here, we propose a simple procedure for calibratingany regression algorithm; when applied to Bayesian and probabilistic models, itis guaranteed to produce calibrated uncertainty estimates given enough data.Our procedure is inspired by Platt scaling and extends previous work onclassification. We evaluate this approach on Bayesian linear regression,feedforward, and recurrent neural networks, and find that it consistentlyoutputs well-calibrated credible intervals while improving performance on timeseries forecasting and model-based reinforcement learning tasks.

Accurate Uncertainties for Deep Learning Using Calibrated Regression