AI RESEARCH
Robust Learning of a Group DRO Neuron
arXiv CS.LG
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ArXi:2601.18115v2 Announce Type: replace We study the problem of learning a single neuron under standard squared loss in the presence of arbitrary label noise and group-level distributional shifts, for a broad family of covariate distributions. Our goal is to identify a ''best-fit'' neuron parameterized by $\mathbf{w}_*$ that performs well under the most challenging reweighting of the groups.