Self-Labeling in Multivariate Causality and Quantification for Adaptive Machine Learning

arXiv:2404.05809v1 Announce Type: new
Abstract: Adaptive machine learning (ML) aims to allow ML models to adapt to ever-changing environments with potential concept drift after model deployment. Traditionally, adaptive ML requires a new dataset to be manually labeled to tailor deployed models to altered data distributions. Recently, an interactive causality based self-labeling method was proposed to autonomously associate causally related data streams for domain adaptation, showing promising results compared to traditional feature similarity-based semi-supervised learning. Several unanswered research questions remain, including …

abstract adapt arxiv causality concept cs.ai cs.lg data dataset deployment drift environments interactive labeling machine machine learning ml models model deployment multivariate quantification stat.me type