arXiv stat.ML
· Papers
Learning with Monotone Adversarial Corruptions
arXiv:2601.02193v2 Announce Type: replace-cross Abstract: We study the extent to which standard machine learning algorithms rely on exchangeability and independence of data by introducing a monotone adversarial corruption model. In this model, an adversary, upon looking at a "clean" i.i.d. dataset, inserts additional "