2 papers
cs.IT2026
Minimax bounds for watermarked and masked recursive discrete distribution estimation
Millen Kanabar, Michael Gastpar
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 e…
cs.IT2025
Model non-collapse: Minimax bounds for recursive discrete distribution estimation
Millen Kanabar, Michael Gastpar
Learning discrete distributions from i.i.d. samples is a well-understood problem. However, advances in generative machine learning prompt an interesting new, non-i.i.d. setting: af…