DataCupid: Stable Matching Repair for Training Data Acquisition with Delayed Feedback
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Computer Science > Machine Learning
arXiv:2610.12960 (cs)
[Submitted on 9 Oct 2026]
Title:DataCupid: Stable Matching Repair for Training Data Acquisition with Delayed Feedback
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Abstract:Matching repair after utility changes can reuse valid observations, but new evaluations must respect matching capacity and may return out of order. We study announced invalidations with bounded, possibly reward-dependent reporting delays and pending evaluations that do not occupy future matching capacity. Residualload repair (RLR) combines sound outcome certificates with batched matching schedules for unequal sample deficits. Its complete dyadic quota schedule admits a sharp issue-round bound with coefficient cb < 1.607 relative to the terminal residual endpoint load, together with separate delay and evidence-lifetime bounds. RLR-P monitors complete issue-order prefixes without changing the sampling plan before acceptance. With a deterministic pure oracle, a single-repair coupling from the same drained initial state guarantees no increase in certification time, issued observations, or time to a reusable checkpoint. We also derive an information-load lower bound. On fixed priority-star markets with unknown Bernoulli gaps, PrioritySearch attains the history-adjusted first-order constant for expected certification time as {\delta} {\sim} 0, under hypothesis-independent linear history scaling and sublogarithmic reporting delays. Paired simulations and measured-feedback replay quantify the trade-offs: in the replay, RLR-P has lower mean capped update-certification time than a monitored width-based baseline, at higher oracle CPU cost.
| Comments: | 2 figures, 8 tables |
| Subjects: | Machine Learning (cs.LG); Information Theory (cs.IT) |
| Cite as: | arXiv:2610.12960 [cs.LG] |
| (or arXiv:2610.12960v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12960
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled DataCupid: Stable Matching Repair for Training Data Acquisition with Delayed Feedback, by He Sun and 4 other authors
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