Bandit Multiclass PAC Learning: Corrected Lower Bounds, Exact Families, and a Confidence Direct-Sum Phenomenon
We study realizable multiclass PAC learning with bandit feedback: the learner observes an i.i.d. instance, predicts one of $K$ labels, and learns only whether the prediction was correct. Hanneke, Meng, Moran, and Shaeiri (arXiv:2605.25678) characterized the optimal sample complexity via the bandit DS dimension $\mathrm{BDS}$ up to logarithmic factors, and asked whether every class admits sample complexity $O((\mathrm{BDS}+\log(1/δ))/ε)$. First, we show that the published lower bound $Ω((\mathrm{BDS}+\log(1/δ))/ε)$ is incorrect as stated: we exhibit explicit classes with $\mathrm{BDS}=K-1$ whose sample complexity is exponentially smaller, and locate two independent gaps in its proof. We re...
| Authors | Guangjian Zhang |
|---|---|
| Primary category | cs.LG |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.