Domain-Agnostic Constrastive Representations for Learning from Label Proportions

Jay Nandy, Rishi Saket, Prateek Jain, Jatin Chauhan, Balaraman Ravindran, Aravindan Raghuveer

Abstract:

We study the weak supervision learning problem of Learning from
Label Proportions (LLP) where the goal is to learn an instance-level
classifier using proportions of various class labels in a bag – a collection of input instances that often can be highly correlated. While
representation learning for weakly-supervised tasks is found to
be effective, they often require domain knowledge. To the best of
our knowledge, representation learning for tabular data (unstructured data containing both continuous and categorical features)
are not studied. In this paper, we propose to learn diverse representations of instances within the same bags to effectively utilize
the weak bag-level supervision. We propose a domain agnostic LLP
method, called "Self Contrastive Representation Learning for LLP"
(SelfCLR-LLP) that incorporates a novel self–contrastive function
as an auxiliary loss to learn representations on tabular data for LLP.
We show that diverse representations for instances within the same
bags aid efficient usage of the weak bag-level LLP supervision. We
evaluate the proposed method through extensive experiments on
real-world LLP datasets from e-commerce applications to demonstrate the effectiveness of our proposed SelfCLR-LLP.