Capacity-constrained online convex optimization with delayed feedback achieves near-standard regret with logarithmic tracking capacity via randomized scheduling and weighted FTRL.
A reduction framework converts online convex optimization with delayed feedback into immediate feedback, improving delay-dependent regret bounds for both first-order and bandit settings via continuous-time decomposition.