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Statistical algorithms and planted satisfiability problems

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If you have a question about this talk, please contact Quentin Berthet.

I will discuss a general framework originating in learning theory for proving average-case algorithmic lower bounds for the class of “statistical algorithms”. I will give an overview of the types of algorithmic approaches that can be implemented in this framework, and then discuss the example of planted random satisfiability problems, for which we can identify a tractability threshold for statistical algorithms. Based on joint work with Vitaly Feldman and Santosh Vempala.

This talk is part of the Statistics series.

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