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dc.contributor.authorBhaskar, Umang
dc.date.accessioned2019-10-31T01:20:57Z
dc.date.available2019-10-31T01:20:57Z
dc.date.issued2019-10-18
dc.identifier.urihttp://hdl.handle.net/1853/62007
dc.descriptionPresented on October 18, 2019 at 11:00 a.m. in the Groseclose Building, Room 402.en_US
dc.descriptionUmang Bhaskar is a faculty member in the School of Technology and Computer Science at the Tata Institute of Fundamental Research. His primary research is on algorithmic game theory, the study of computational problems that arise when multiple rational agents interact, each trying to optimize its own objective.en_US
dc.descriptionRuntime: 56:41 minutesen_US
dc.description.abstractIn partial function extension, we are given a partial function consisting of points from a domain and a function value at each point. Our objective is to determine if this partial function can be extended to a total function defined on the domain, that additionally satisfies a given property, such as convexity. This basic problem underlies research questions in many areas, such as learning, property testing, and game theory. We present bounds on the complexity of partial function extension to subadditive, submodular, and convex functions, and present applications to learning as well as property testing for these functions. This is joint work with Gunjan Kumar.en_US
dc.format.extent56:41 minutes
dc.language.isoen_USen_US
dc.relation.ispartofseriesARC Colloquiumen_US
dc.subjectLearningen_US
dc.subjectPartial function extensionen_US
dc.titlePartial Function Extension with Applications to Learning and Property Testingen_US
dc.typeLectureen_US
dc.typeVideoen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Algorithms, Randomness and Complexity Centeren_US
dc.contributor.corporatenameTata Institute of Fundamental Researchen_US


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  • ARC Talks and Events [88]
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