When Does More Correct Data Hurt? Insertion-Stability and the Limits of Dimension-Based Theory
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 factor above the clean PAC rate. Because that rate is a worst case over all classes, it says nothing about which classes actually suffer the penalty, and the answer turns on the learner. We call a learner insertion-stable if feeding it more correct...
| Authors | Joseph Sankoorikal Johny |
|---|---|
| Primary category | cs.LG |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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