Can learning algorithms take in data along with their uncertainty? (chaining ML algorithms along with errors)
One option is to use Bayesian methods in machine learning.
Assume the input to any stage in a machine learning pipeline is a prior distribution and the output of any stage is a posterior distribution. Then it would be possible to chain stages together with uncertainty propagated through the system through the distributions.