Conformal online model aggregation

arXiv:2403.15527v1 Announce Type: cross
Abstract: Conformal prediction equips machine learning models with a reasonable notion of uncertainty quantification without making strong distributional assumptions. It wraps around any black-box prediction model and converts point predictions into set predictions that have a predefined marginal coverage guarantee. However, conformal prediction only works if we fix the underlying machine learning model in advance. A relatively unaddressed issue in conformal prediction is that of model selection and/or aggregation: for a given problem, which of the …

abstract aggregation arxiv assumptions box coverage cs.lg however machine machine learning machine learning models making notion prediction predictions quantification set stat.ml type uncertainty