PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting
We study the problem of multiclass PAC learning with bandit feedback in the realizable setting. In this framework, there is an unknown data distribution over an instance space $\mathcal{X}$ and a label space $\mathcal{Y}$, as in classical multiclass PAC learning, but the learner does not observe the labels of the i.i.d. training examples. Instead, in each round, it receives an unlabeled instance, predicts its label, and receives bandit feedback indicating only whether the prediction is correct. Despite this restriction, the goal remains the same as in classical PAC learning. We provide a general characterization of the optimal sample complexity of this problem, sharp for every non-trivial...
| Authors | Steve Hanneke, Qinglin Meng, Shay Moran et al. |
|---|---|
| Primary category | stat.ML |
| Published | 25 May 2026 |
| Last updated at source | 4 October 2026 |
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