  Our goal in this paper is to exploit heteroscedastic temperature scaling as a
calibration strategy for out of distribution (OOD) detection.
Heteroscedasticity here refers to the fact that the optimal temperature
parameter for each sample can be different, as opposed to conventional
approaches that use the same value for the entire distribution. To enable this,
we propose a new training strategy called anchoring that can estimate
appropriate temperature values for each sample, leading to state-of-the-art OOD
detection performance across several benchmarks. Using NTK theory, we show that
this temperature function estimate is closely linked to the epistemic
uncertainty of the classifier, which explains its behavior. In contrast to some
of the best-performing OOD detection approaches, our method does not require
exposure to additional outlier datasets, custom calibration objectives, or
model ensembling. Through empirical studies with different OOD detection
settings -- far OOD, near OOD, and semantically coherent OOD - we establish a
highly effective OOD detection approach. Code to reproduce our results is
available at github.com/LLNL/AMP
