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Спутники
РН
SpaceX
Роскосмос
NASA
СпутникиРНSpaceXРоскосмосNASA
Corruption is notoriously widespread in data collection. Despite extensive research, the existing literature predominantly focuses on specific settings and learning scenarios, lacking a
unified view of corruption modelization and mitigation. In this work, we develop a general
theory of corruption, which incorporates all modifications to a supervised learning problem,
including changes in model class and loss. Focusing on changes to the underlying probability distributions via Markov kernels, our approach leads to three novel opportunities.
First, it enables the construction of a novel, provably exhaustive corruption framework,
distinguishing among different corruption types. This serves to unify existing models and
establish a consistent nomenclature. Second, it facilitates a systematic analysis of corruption consequences on learning tasks, by considering Bayes risks in the clean and corrupted
scenarios. Notably, while label corruptions affect only the loss function, attribute corruptions additionally influence the hypothesis class. Third, building upon these results, we
investigate mitigations for various corruption types. We expand existing loss-correction
methods for label corruption to handle dependent corruption types. Our findings highlight
the necessity to generalize this classical corruption-corrected learning framework to a new
paradigm with weaker requirements to encompass more corruption types. We provide such
a paradigm as well as loss correction formulas in the attribute and joint corruption cases.