robust statistics

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.
Monday, January 25, 2016 - 10:15am - 11:05am
Aleksandr Aravkin (University of Washington)
We present a modeling framework for a wide range of large-scale optimization problems in data science, and show how conjugate representations can be exploited to design an interior point approach for this class. We then show several applications, with emphasis on modeling and problem structure, and discuss matrix-free extensions for large-scale problems.
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