2 papers
cs.LG2026
Unsupervised Representation Learning - an Invariant Risk Minimization Perspective
Yotam Norman, Ron Meir
We propose a novel unsupervised framework for \emph{Invariant Risk Minimization} (IRM), extending the concept of invariance to settings where labels are unavailable. Traditional IR…
cs.LG2025
Unsupervised Feature Selection Through Group Discovery
Shira Lifshitz, Ofir Lindenbaum, Gal Mishne +2
Unsupervised feature selection (FS) is essential for high-dimensional learning tasks where labels are not available. It helps reduce noise, improve generalization, and enhance inte…