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Robustness Meets Algorithms
From Communications of the ACM

Robustness Meets Algorithms

We give the first efficient algorithm for estimating the parameters of a high-dimensional Gaussian that is able to tolerate a constant fraction of corruptions that... Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
From Communications of the ACM | May 2021
Learning Topic Models - Provably and Efficiently
From Communications of the ACM

Learning Topic Models - Provably and Efficiently

This article shows that some new theoretical algorithms that have provable guarantees can be adapted to yield highly practical tools for topic modeling. Sanjeev Arora, Rong Ge, Yoni Halpern, David Mimno, Ankur Moitra, David Sontag, Yichen Wu, Michael Zhu
From Communications of the ACM | April 2018
Disentangling Gaussians
From Communications of the ACM

Disentangling Gaussians

The Gaussian mixture model is one of the oldest and most widely used statistical models. Our work focuses on the case where the mixture consists of a small but... Adam Tauman Kalai, Ankur Moitra, Gregory Valiant
From Communications of the ACM | February 2012
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