Batched Predictors Generalize within Distribution
Andreas Loukas Pan Kessel
We study the generalization properties of batched predictors, i.e., models
tasked with predicting the mean label of a small set (or batch) of examples.
The batched prediction paradigm is particularly relevant for models deployed to
determine the quality of a group of compounds in preparation for offline
testing. By utilizing a suitable generalization of the Rademacher complexity,
we prove that batched predictors come with exponentially stronger
generalization guarantees as compared to the standard per-sample approach.
Surprisingly, the proposed bound holds independently of overparametrization.
Our theoretical insights are validated experimentally for various tasks,
architectures, and applications.