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Median-of-Means as an Extremal Convex Estimator and a Nonconvex Route to the Trimmed Oracle
We revisit median-of-means estimation from a deterministic optimization viewpoint and develop a family of block-Lp estimators for robust learning with heavy-tailed and adversarially corrupted data. In a block contamination model with at least a fraction 1 minus epsilon of good blocks, we first show
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