  Owing to the prohibitive costs of generating large amounts of labeled data,
programmatic weak supervision is a growing paradigm within machine learning. In
this setting, users design heuristics that provide noisy labels for subsets of
the data. These weak labels are combined (typically via a graphical model) to
form pseudolabels, which are then used to train a downstream model. In this
work, we question a foundational premise of the typical weakly supervised
learning pipeline: given that the heuristic provides all ``label" information,
why do we need to generate pseudolabels at all? Instead, we propose to directly
transform the heuristics themselves into corresponding loss functions that
penalize differences between our model and the heuristic. By constructing
losses directly from the heuristics, we can incorporate more information than
is used in the standard weakly supervised pipeline, such as how the heuristics
make their decisions, which explicitly informs feature selection during
training. We call our method Losses over Labels (LoL) as it creates losses
directly from heuristics without going through the intermediate step of a
label. We show that LoL improves upon existing weak supervision methods on
several benchmark text and image classification tasks and further demonstrate
that incorporating gradient information leads to better performance on almost
every task.
