arxiv
PublishedSeptember 10, 2026 at 4:00 AM
Constrained Online Learning with Noisy Constraint Values
Publisher summary· verbatim
arXiv:2609.06921v1 Announce Type: cross Abstract: We study constrained online convex optimization with adversarial constraints when constraint values and gradients are observed through unbiased noise. Gaussian value noise of standard deviation $\sigma$ yields a worst-case lower bound of $\Omega(\min
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Originally published on arxiv ↗