Useful to Whom? The Easy-versus-Coverage Boundary Moves with the Learner
Is the same data equally useful to every learner? The question is whether the value of a training sample belongs to the sample or arises in its interaction with the learner. We test it with a selection experiment. Under a per-class budget, two kinds of samples compete for the slots: easy samples, which a short training run learns first, and coverage samples, which spread over the class in feature space. Easy samples win at small budgets and coverage samples at large ones, and the budget at which the winner flips, the crossover m0, can be measured. We ask whether m0 is set by the data or by the learner. To separate the two, we freeze the selected subsets and change only the learner that tr...
| Authors | Yangze Liu, Xiao-Long Yin, Zhongyi Han |
|---|---|
| Primary category | cs.LG |
| Published | 22 September 2026 |
| Last updated at source | 9 October 2026 |
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