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arXiv Machine Learning By Sam Urmian, Qinyi Liu, Mohammad Khalil

Decoupled Learning and Selection in Slate Recommendation for Privacy and Stability Under Noisy Scores

Read the original on arXiv Machine Learning →

The paper formalizes slate recommendation as a two-stage process: a randomized score learner followed by deterministic selection. It shows that a differential‑privacy guarantee can be preserved through the selection stage when selector inputs are public or independent, and introduces a logged margin certificate that bounds score‑induced objective changes, ensuring the slate order remains stable. Experiments on OULAD, MovieLens‑25M, and Amazon Musical Instruments demonstrate that higher anchor weight reduces ranking churn, while simulations confirm bounded target drift and utility dependent on settings.

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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