  Accurate quantification of uncertainty is crucial for real-world applications
of machine learning. However, modern deep neural networks still produce
unreliable predictive uncertainty, often yielding over-confident predictions.
In this paper, we are concerned with getting well-calibrated predictions in
regression tasks. We propose the calibrated regression method using the maximum
mean discrepancy by minimizing the kernel embedding measure. Theoretically, the
calibration error of our method asymptotically converges to zero when the
sample size is large enough. Experiments on non-trivial real datasets show that
our method can produce well-calibrated and sharp prediction intervals, which
outperforms the related state-of-the-art methods.
