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

arXiv · AI, language, vision and robotics · article · Sep 24, 2026 · UTC

We formalize slate recommendation as a randomized score learner followed by deterministic selection. First, an appropriately scoped differential-privacy guarantee passes through selection and its audit trace by post-processing. End-to-end privacy holds only when selector inputs are public or independent, previous private outputs, or separately privacy-accounted; fixing raw state or candidate information instead yields only a conditional guarantee. Second, we derive a logged margin certificate: bounded score-induced objective movement below half the smallest greedy decision margin guarantees th

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