Privacy Preserving Recalibration under Domain Shift

21 Aug 2020Rachel LuoShengjia ZhaoJiaming SongJonathan KuckStefano ErmonSilvio Savarese

Classifiers deployed in high-stakes real-world applications must output calibrated confidence scores, i.e. their predicted probabilities should reflect empirical frequencies. Recalibration algorithms can greatly improve a model's probability estimates; however, existing algorithms are not applicable in real-world situations where the test data follows a different distribution from the training data, and privacy preservation is paramount (e.g. protecting patient records)... (read more)

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