arXiv:2606.11711v1 Announce Type: new Abstract: Online learning with delayed feedback typically assumes that the learner can track all pending rounds until their feedback arrives. In practice, tracking resources are finite, and feedback from untracked rounds is permanently lost. In this paper, we study delayed online convex optimization (OCO) under a hard capacity constraint, where at most $C$ pending rounds can be tracked at any time. To model delay information, we introduce a semi-clairvoyant model that refines the clairvoyant assumption from prior work: rather than requiring delays to be kn
The increasing complexity and scale of online learning systems, especially in AI, necessitate more robust solutions for managing constrained resources and delayed information.
This research addresses a fundamental limitation in practical AI system design, pushing towards more efficient and reliable real-world applications where tracking resources are finite.
Current online learning models can become significantly more resource-efficient and robust by explicitly accounting for capacity constraints and improving the handling of delayed feedback.
- · AI developers
- · cloud computing providers
- · autonomous systems
More efficient and cost-effective deployment of AI models in environments with limited tracking capacity.
Enables new applications of AI in real-time, resource-constrained settings like edge computing or robotics where current systems struggle.
Potentially democratizes advanced AI deployment by reducing the computational and memory overhead required for complex online learning tasks.
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Read at arXiv cs.LG