Campuses:

efficient algorithms

Tuesday, June 18, 2019 - 9:00am - 9:50am
Ilias Diakonikolas (University of Southern California)
Fitting a model to a collection of observations is one of the quintessential questions in statistics. The standard assumption is that the data was generated by a model of a given type (e.g., a mixture model). This simplifying assumption is at best only approximately valid, as real datasets are typically exposed to some source of contamination. Hence, any estimator designed for a particular model must also be robust in the presence of corrupted data.
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