Capacity-Constrained Online Convex Optimization with Delayed Feedback
Researchers have introduced a theoretical framework for online convex optimization (OCO) that operates under a hard capacity constraint, where only a fixed number of pending feedback rounds can be tracked at once. Prior work on delayed OCO assumed unlimited tracking resources, an assumption that breaks down in practical systems with finite memory or compute. The work provides the first regret guarantees for this capacity-constrained setting, showing that even modest capacity can recover near-standard learning rates.
A paper submitted to arXiv introduces a new model for online convex optimization with delayed feedback, addressing the realistic scenario where a learner can track at most C pending rounds simultaneously, with feedback from untracked rounds permanently lost. To handle uncertainty about when feedback will arrive, the authors propose a 'semi-clairvoyant' model that relaxes the strong clairvoyant assumption used in prior work, instead allowing the learner to observe delay expirations as they occur. Their approach reduces the capacity-constrained problem to a novel 'delayed and weighted' OCO problem, using a randomized scheduler that assigns importance weights to tracked observations. For this base problem, they develop Delayed-Weighted Follow-the-Regularized-Leader (FTRL) algorithms and their bandit analogues, with regret bounds that explicitly capture the interplay between time-varying weights and delays. Key results show that for first-order feedback, capacity C = Ω(log T) suffices to match standard delayed OCO rates up to logarithmic factors, while bandit feedback regret degrades gracefully as a function of the ratio of maximum pending observations to capacity, remaining sublinear throughout.
What's missing
The paper is a theoretical contribution and does not include empirical experiments validating the regret bounds on real or simulated systems. The tightness of the bounds (i.e., whether matching lower bounds exist for the capacity-constrained setting) is not established in the abstract.
What different sources said
- arXiv cs.LGCenter
Capacity-Constrained Online Convex Optimization with Delayed Feedback
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