Oracle-Efficient Online Classification with Stochastic Inputs and Adversarial Outputs
We consider binary prediction with i.i.d. contexts from an unknown distribution and adaptively chosen losses. We show that a simple Follow-the-Perturbed-Leader algorithm using a Gaussian perturbation for each observed context achieves $\widetilde O(\sqrt{T\log N})$ regret for a class of $N$ experts, while requiring one optimization-oracle call per round and no explicit enumeration of the class. For an infinite hypothesis class $\mathcal H$, the same algorithm achieves $\widetilde O(\sqrt{T\operatorname{VC}(\mathcal H)})$ regret. This resolves an open problem posed by Lazaric and Munos (2012), showing that hybrid classification is computationally as easy as statistical learning. As an appl...
| Authors | Gon Buzaglo, Elad Hazan |
|---|---|
| Primary category | cs.LG |
| Published | 27 September 2026 |
| Last updated at source | 5 October 2026 |
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