AI RESEARCH
Price of Quality: Sufficient Conditions for Sparse Recovery using Mixed-Quality Data
arXiv CS.LG
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ArXi:2605.10713v1 Announce Type: cross We study sparse recovery when observations come from mixed-quality sources: a small collection of high-quality measurements with small noise variance and a larger collection of lower-quality measurements with higher variance. For this heterogeneous-noise setting, we establish sample-size conditions for information-theoretic and algorithmic recovery.