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arXiv Machine Learning By He Sun, Junyuan Mao, Wenhao Jiang, Mingjun Xiao, Shinan Liu

DataCupid: Stable Matching Repair for Training Data Acquisition with Delayed Feedback

Read the original on arXiv Machine Learning →

The paper introduces RLR, a stable matching repair method that reuses valid observations while respecting matching capacity and handling delayed, out‑of‑order evaluations. RLR combines sound outcome certificates with batched matching schedules to manage unequal sample deficits, achieving a sharp issue‑round bound and separate delay and evidence‑lifetime bounds. The authors also present RLR‑P, a variant that monitors issue‑order prefixes without altering the sampling plan, and demonstrate through simulations that it reduces mean capped update‑certification time compared to a width‑based baseline, albeit with higher oracle CPU cost.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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