  We present a new loss function that addresses the out-of-distribution (OOD)
calibration problem. While many objective functions have been proposed to
effectively calibrate models in-distribution, our findings show that they do
not always fare well OOD. Based on the Principle of Maximum Entropy, we
incorporate helpful statistical constraints observed during training,
delivering better model calibration without sacrificing accuracy. We provide
theoretical analysis and show empirically that our method works well in
practice, achieving state-of-the-art calibration on both synthetic and
real-world benchmarks.
