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- W3040893584 abstract "In this work, we study some novel applications of conformal inference techniques to the problem of providing machine learning procedures with more transparent, accurate, and practical performance guarantees. We provide a natural extension of the traditional conformal prediction framework, done in such a way that we can make valid and well-calibrated predictive statements about the future performance of arbitrary learning algorithms, when passed an as-yet unseen training set. In addition, we include some nascent empirical examples to illustrate potential applications." @default.
- W3040893584 created "2020-07-16" @default.
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- W3040893584 date "2020-07-09" @default.
- W3040893584 modified "2023-09-23" @default.
- W3040893584 title "Making learning more transparent using conformalized performance prediction." @default.
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