Signals 4 · free daily AI digest

Minimax bounds for watermarked and masked recursive discrete distribution estimation

Paper recorded by Signals 4 on 2026-08-31 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-08-31 on arXiv · recorded by Signals 4 on 2026-09-01

Category: cs.LG · 机器学习 · first seen 2026-09-01

Abstract

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

Read on arXiv →

#175 most recent of 215 cs.LG papers we have recorded · ↑ newer: One Adapter, Many Tasks: Task-Conditioned Feature Transformations for · ↓ older: Sycophantic Agreement Transfers with Neutral Data via Contrastive Pref
Cite this page: Minimax bounds for watermarked and masked recursive discrete distribution estimation: the #175 most recent of 215 cs.LG papers we have recorded (as of 2026-08-31). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/minimax-bounds-for-watermarked-and-masked-recursive-discrete-distribution-estima.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.LG papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions