  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 plethora of prediction methods (random forests, neural nets,
regularized linear models, etc.) should we conformalize? This paper proposes a
new approach towards conformal model aggregation in online settings that is
based on combining the prediction sets from several algorithms by voting, where
weights on the models are adapted over time based on past performance.
