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This paper revisits median-of-means estimation from a deterministic optimization perspective and develops a family of block-Lp estimators for robust learning under heavy-tailed and adversarial corruption, showing that nonconvex methods can approach the trimmed oracle performance while remaining computationally tractable.
Proposes a new RANSAC scoring function that marginalizes the inlier scale analytically, removing the need for user-supplied parameters. The method achieves state-of-the-art accuracy on a benchmark of nearly 70,000 image pairs.