A robust assessment for invariant representations

arXiv:2404.05058v1 Announce Type: new
Abstract: The performance of machine learning models can be impacted by changes in data over time. A promising approach to address this challenge is invariant learning, with a particular focus on a method known as invariant risk minimization (IRM). This technique aims to identify a stable data representation that remains effective with out-of-distribution (OOD) data. While numerous studies have developed IRM-based methods adaptive to data augmentation scenarios, there has been limited attention on directly assessing how …

abstract arxiv assessment challenge cs.lg data focus identify machine machine learning machine learning models performance representation risk robust stat.ml type