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Poster
Outlier Robust Mean Estimation with Subgaussian Rates via Stability
Ilias Diakonikolas · Daniel M. Kane · Ankit Pensia

Tue Dec 08 09:00 AM -- 11:00 AM (PST) @ Poster Session 1 #519
in Poster Session 2 »

We study the problem of outlier robust high-dimensional mean estimation under a finite covariance assumption, and more broadly under finite low-degree moment assumptions. We consider a standard stability condition from the recent robust statistics literature and prove that, except with exponentially small failure probability, there exists a large fraction of the inliers satisfying this condition. As a corollary, it follows that a number of recently developed algorithms for robust mean estimation, including iterative filtering and non-convex gradient descent, give optimal error estimators with (near-)subgaussian rates. Previous analyses of these algorithms gave significantly suboptimal rates. As a corollary of our approach, we obtain the first computationally efficient algorithm for outlier robust mean estimation with subgaussian rates under a finite covariance assumption.

Author Information

Ilias Diakonikolas (UW Madison)
Daniel M. Kane (UCSD)
Ankit Pensia (University of Wisconsin-Madison)

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