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• W3080339034 abstract "Classifiers deployed in high-stakes applications must output calibrated confidence scores, i.e. their predicted probabilities should reflect empirical frequencies. Typically this is achieved with recalibration algorithms that adjust probability estimates based on the real-world data; 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). We introduce a framework that provides abstractions for performing recalibration under differential privacy constraints. This framework allows us to adapt existing recalibration algorithms to satisfy differential privacy while remaining effective for domain-shift situations. Guided by our framework, we also design a novel recalibration algorithm, accuracy temperature scaling, that is tailored to the requirements of differential privacy. In an extensive empirical study, we find that our algorithm improves calibration on domain-shift benchmarks under the constraints of differential privacy. On the 15 highest severity perturbations of the ImageNet-C dataset, our method achieves a median ECE of 0.029, over 2x better than the next best recalibration method and almost 5x better than without recalibration." @default.
• W3080339034 created "2020-09-01" @default.
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• W3080339034 date "2021-05-04" @default.
• W3080339034 modified "2023-09-27" @default.
• W3080339034 title "Privacy Preserving Recalibration under Domain Shift" @default.
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• W3080339034 hasPublicationYear "2021" @default.
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