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Learning from data in the presence of outliers is a fundamental problem in statistics. In this work, we study robust statistics in the presence of overwhelming outliers for the fundamental problem of subspace recovery. Given a dataset where an α fraction (less than half) of the data is distributed uniformly in an unknown k dimensional subspace in d dimensions, and with no additional assumptions on the remaining data, the goal is to recover a succinct list of O(1/α) subspaces one of which isarXiv:2002.03004v1 fatcat:hwlg67ldsrbwhcjtbqnwj5lmkm