New Directions in Property Testing

Property testing algorithms seek to determine whether an unknown massive object has some particular property of interest, or is “far” from having the property, while inspecting only a tiny portion of the object. Recent years have witnessed significant progress on both classic property testing problems and the development of several new property testing problems and frameworks, motivated by connections to machine learning theory and high-dimensional data analysis. In this talk, Rocco Servedio will survey several of these new property testing problems, models, and results.

Speaker: Rocco Servedio, Columbia University

Register at weblink to attend in person.  Lecture will be available on YouTube later (see weblink)