Universal Online Convex Optimization With Delayed Feedback
We study universal online convex optimization with delayed feedback. We develop a single algorithm that simultaneously adapts to general convex, strongly convex, and exp-concave losses without prior knowledge of the curvature class or parameters. Our regret bounds match the best known dependence on the time horizon $T$ and delays up to doubly logarithmic factors: roughly $\sqrt{T + d_{\mathrm{tot}}}$ for convex losses and $σ_{\max}\log T$ rates with curved losses, where $d_{\mathrm{tot}}$ is the total cumulative delay and $σ_{\max}$ is the maximum number of simultaneously missing observations. Our main technical contribution is a delayed meta-learner with a second-order excess-loss guaran...
| Authors | Hao Qiu, Andrew Jacobsen, Emmanuel Esposito et al. |
|---|---|
| Primary category | cs.LG |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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