Search for tag: "mathematical sciences"
ORIE Reunion 2020 (Class of 1986-1995)Zoom Recording ID: 94656006119 UUID: YYcvJ5p5Sny71bwp7/yatA== Meeting Time: 2020-06-06T18:30:00Z
From Henry Lam
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ORIE Colloquium on 4/7/2015 - William Thomas: Why Post-World War II OR Theory Developed So RapidlyThe fifteen years following World War II saw the development of a wide range of theories of decision making and resource allocation. Notable fields arising from slim foundations included: sequential…
From E. Cornelius
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ORIE Colloquium on 3/25/2014 - Rob Freund: A First-Order View of Some Boosting Methods: Computational Guarantees and Connections to RegularizationTuesday, March 25, 2014 at 4:15pm Frank H. T. Rhodes Hall, 253 ORIE Colloquium: Rob Freund (MIT) - A First-Order View of Some Boosting Methods: Computational Guarantees and Connections to…
From E. Cornelius
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CAM Colloquium: Peter Frazier - Optimal Learning for Molecular DiscoveryScientists use laboratory experiments to search for molecules with desirable properties, e.g., that make efficient solar cells; or that cure cancer. The success of such a search hinges on making…
From E. Cornelius
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ORIE Colloquium, 2013-01-24 - Joline Uichanco (MIT): Business Analytics for Flexible Resource Allocation Under Random EmergenciesORIE Colloquium: Joline Uichanco (MIT) - Business Analytics for Flexible Resource Allocation Under Random Emergencies Thursday, January 24, 2013 at 4:15pm Frank H. T. Rhodes Hall, 253 In this work…
From E. Cornelius
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CAM Colloquium 2013-02-01, Mason Porter (edit)CAM Colloquium: Mason Porter (Oxford) - Cascades and Social Influence on Networks Friday, February 1, 2013 at 3:30pm Frank H. T. Rhodes Hall, 655 Cascades and Social Influence on NetworksI discuss…
From E. Cornelius
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CAM Colloquium 2013-02-01, Mason PorterCAM Colloquium: Mason Porter (Oxford) - Cascades and Social Influence on Networks Friday, February 1, 2013 at 3:30pm Frank H. T. Rhodes Hall, 655 Cascades and Social Influence on NetworksI discuss…
From E. Cornelius
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ORIE Colloquium on 11/29/2011 - John Birge: Monte Carlo and Bayesian Methods for Stochastic Dynamic ProgramsStandard approaches to solving stochastic dynamic programs suffer from the curse of dimensionality. Taking advantage of structural properties such as convexity can help reduce the effect of dimension…
From E. Cornelius
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