May 13, 2024, 4:42 a.m. | Drew Prinster, Samuel Stanton, Anqi Liu, Suchi Saria

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.06627v1 Announce Type: new
Abstract: As machine learning (ML) gains widespread adoption, practitioners are increasingly seeking means to quantify and control the risk these systems incur. This challenge is especially salient when ML systems have autonomy to collect their own data, such as in black-box optimization and active learning, where their actions induce sequential feedback-loop shifts in the data distribution. Conformal prediction has emerged as a promising approach to uncertainty and risk quantification, but existing variants either fail to accommodate …

abstract active learning adoption arxiv autonomy box challenge control cs.ai cs.lg data distribution machine machine learning optimization risk stat.ml systems type

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