  Predicting user response probabilities is vital for ad ranking and bidding.
We hope that predictive models can produce accurate probabilistic predictions
that reflect true likelihoods. Calibration techniques aim to post-process model
predictions to posterior probabilities. Field-level calibration -- which
performs calibration w.r.t. to a specific field value -- is fine-grained and
more practical. In this paper we propose a doubly-adaptive approach AdaCalib.
It learns an isotonic function family to calibrate model predictions with the
guidance of posterior statistics, and field-adaptive mechanisms are designed to
ensure that the posterior is appropriate for the field value to be calibrated.
Experiments verify that AdaCalib achieves significant improvement on
calibration performance. It has been deployed online and beats previous
approach.
