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This content will become publicly available on April 25, 2026

Title: A Unifying Framework for Representation Learning
As the field of representation learning grows, there has been a proliferation of different loss functions to solve different classes of problems. We introduce a single information-theoretic equation that generalizes a large collection of mod- ern loss functions in machine learning. In particular, we introduce a framework that shows that several broad classes of machine learning methods are precisely minimizing an integrated KL divergence between two conditional distributions: the supervisory and learned representations. This viewpoint exposes a hidden information geometry underlying clustering, spectral methods, dimensionality re- duction, contrastive learning, and supervised learning. This framework enables the development of new loss functions by combining successful techniques from across the literature. We not only present a wide array of proofs, connecting over 23 different approaches, but we also leverage these theoretical results to create state-of-the-art unsupervised image classifiers that achieve a +8% improvement over the prior state-of-the-art on unsupervised classification on ImageNet-1K. We also demonstrate that I-Con can be used to derive principled debiasing methods which improve contrastive representation learners.  more » « less
Award ID(s):
1955864
PAR ID:
10646690
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
International Conference on Learning Representations 2025
Date Published:
Format(s):
Medium: X Size: 9MB
Size(s):
9MB
Sponsoring Org:
National Science Foundation
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