Domain-Agnostic Contrastive Representations for Learning from Label Proportions

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. 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.