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In this work, we study some novel applications of conformal inferencetechniques to the problem of providing machine learning procedures with moretransparent, accurate, and practical performance guarantees. We provide anatural extension of the traditional conformal prediction framework, done insuch a way that we can make valid and well-calibrated predictive statementsabout the future performance of arbitrary learning algorithms, when passed anas-yet unseen training set. In addition, we include some nascent empiricalexamples to illustrate potential applications.

Making learning more transparent using conformalized performance prediction