Prof. Emo Welzl and Prof. Bernd Gärtner
|Mittagsseminar Talk Information|
Date and Time: Thursday, March 15, 2018, 12:15 pm
Duration: 30 minutes
Location: CAB G51
Speaker: David Steurer
We develop efficient algorithms for clustering and robust moment estimation. Via convex sum-of-squares relaxations, our algorithms can exploit bounds on higher-order moments of the underlying distributions. In this way, our algorithms achieve significantly stronger guarantees than previous approaches.
For example, our clustering algorithm can learn mixtures of k spherical Gaussians in time nO(t) as long as the means have minimum separation t1/2k1/t for any t ≥ 4, significantly improving over the minimum separation k1/4 required by the previous best algorithm due to Vempala and Wang (JCSS’04).
Given corrupted samples that contain a constant fraction of adversarial outliers, our moment estimation algorithms can approximate low-degree moments of unknown distributions, achieving guarantees that match information-theoretic lower-bounds for a broad class of distributions. These algorithms allow us to enhance in a black-box way many previous learning algorithms based on the method of moments, e.g., for independent component analysis and latent dirichlet allocation, such that they become resilient to adversarial outliers.
Based on joint works with Pravesh Kothari and Jacob Steinhardt.
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