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Recent Advances in Algorithmic HighDimensional Robust StatisticsSeminar: Neyman Seminar  February 21  45 p.m.  1011 Evans Hall Ilias Diakonikolas, USC Fitting a model to a collection of observations is one of the quintessential problems in machine learning. Since any model is only approximately valid, an estimator that is useful in practice must also be robust in the presence of model misspecification. It turns out that there is a striking tension between robustness and computational efficiency. Even for the most basic highdimensional tasks, such as robustly computing the mean and covariance, until recently the only known estimators were either hard to compute or could only tolerate a negligible fraction of errors. 5106422781 

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