Title:
Partial Function Extension with Applications to Learning and Property Testing
Partial Function Extension with Applications to Learning and Property Testing
dc.contributor.author | Bhaskar, Umang | |
dc.contributor.corporatename | Georgia Institute of Technology. Algorithms, Randomness and Complexity Center | en_US |
dc.contributor.corporatename | Tata Institute of Fundamental Research | en_US |
dc.date.accessioned | 2019-10-31T01:20:57Z | |
dc.date.available | 2019-10-31T01:20:57Z | |
dc.date.issued | 2019-10-18 | |
dc.description | Presented on October 18, 2019 at 11:00 a.m. in the Groseclose Building, Room 402. | en_US |
dc.description | Umang 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.description | Runtime: 56:41 minutes | en_US |
dc.description.abstract | In 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.extent | 56:41 minutes | |
dc.identifier.uri | http://hdl.handle.net/1853/62007 | |
dc.language.iso | en_US | en_US |
dc.relation.ispartofseries | Algorithms and Randomness Center (ARC) Colloquium | |
dc.subject | Learning | en_US |
dc.subject | Partial function extension | en_US |
dc.title | Partial Function Extension with Applications to Learning and Property Testing | en_US |
dc.type | Moving Image | |
dc.type.genre | Lecture | |
dspace.entity.type | Publication | |
local.contributor.corporatename | Algorithms and Randomness Center | |
local.contributor.corporatename | College of Computing | |
local.relation.ispartofseries | ARC Colloquium | |
relation.isOrgUnitOfPublication | b53238c2-abff-4a83-89ff-3e7b4e7cba3d | |
relation.isOrgUnitOfPublication | c8892b3c-8db6-4b7b-a33a-1b67f7db2021 | |
relation.isSeriesOfPublication | c933e0bc-0cb1-4791-abb4-ed23c5b3be7e |
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