Mining Invariance from Nonlinear Multi-Environment Data: Binary Classification

arXiv:2404.15245v1 Announce Type: cross
Abstract: Making predictions in an unseen environment given data from multiple training environments is a challenging task. We approach this problem from an invariance perspective, focusing on binary classification to shed light on general nonlinear data generation mechanisms. We identify a unique form of invariance that exists solely in a binary setting that allows us to train models invariant over environments. We provide sufficient conditions for such invariance and show it is robust even when environmental …

abstract arxiv binary classification cs.lg data environment environments form general identify light making mining multiple perspective predictions stat.me training type unique