When Does More Correct Data Hurt? Insertion-Stability and the Limits of Dimension-Based Theory

Excerpt

arXiv:2608.14020v1 Announce Type: new Abstract: Adding data known to be correct ought to be safe. Not always. Larsen, Pabbaraju and Shetty model the failure with a monotone adversary, which reads an i.i.d. training sample and may append as many further examples as it likes, provided the target hypothesis labels them all. Mehrotra has since settled the cost, showing that for classes of VC dimension d >= 2 no learner can guarantee expected error better than Theta((d/n)log(en/d)), a logarithmic fac