SOURCE-LINKED INTELLIGENCE
Minimax bounds for watermarked and masked recursive discrete distribution estimation
Watermarking has been proposed as a way to identify synthetic samples in estimation settings where no metadata is available to distinguish them from real samples, but its precise effects remain unexplored. In the absence of a distinguishing mechanism, it has been shown that adding synthetic samples significantly reduces the marginal efficacy of new real samples. In this work, we study the minimax loss of such recursive discrete distribution estimation in the presence of watermarks in contrast to the unassisted and oracle-assisted losses. When the fraction of real samples vanishes asymptoticall
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-31T17:00:09.000Z
First collected: 2026-09-21T06:41:57.136Z. This is not the publication date.