Calibration in One Go: Jointly Learning Accurate and Calibrated Neural Networks
Read the original on arXiv AI →The paper proposes a bilevel Stackelberg formulation that trains standard post‑hoc calibrators end‑to‑end with the neural network backbone, eliminating the need for a separate calibration dataset or dedicated loss. By jointly optimizing accuracy and calibration with cross‑entropy and a single calibrator learning‑rate hyperparameter, the method can use any calibrator type, but shows that the final classification layer alone suffices to produce accurate, well‑calibrated models. This approach outperforms state‑of‑the‑art calibrators while adding minimal complexity and avoiding the overhead of separate calibration components, especially beneficial in multi‑task learning settings.
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