Corruptions of Supervised Learning Problems: Typology and Mitigations

arXiv:2307.08643v2 Announce Type: replace
Abstract: Corruption is notoriously widespread in data collection. Despite extensive research, the existing literature on corruption predominantly focuses on specific settings and learning scenarios, lacking a unified view. There is still a limited understanding of how to effectively model and mitigate corruption in machine learning problems. In this work, we develop a general theory of corruption from an information-theoretic perspective - with Markov kernels as a foundational mathematical tool. We generalize the definition of corruption beyond …

abstract arxiv collection corruption cs.lg data data collection literature machine machine learning research stat.ml supervised learning type understanding view