Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization

A crucial problem in modern data science is data-driven algorithm design, where the goal is to choose the best algorithm, or algorithm parameters, for a specific application domain. In practice, we often optimize over a parametric algorithm family, searching for parameters with high performance on a...

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Veröffentlicht in:	2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS) S. 603 - 614
Hauptverfasser:	Balcan, Maria-Florina, Dick, Travis, Vitercik, Ellen
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.10.2018
Schlagworte:	algorithm design Approximation algorithms Clustering algorithms differentially private optimization Dispersion Greedy algorithms online optimization Optimization piecewise Lipschitz Pricing Privacy
ISSN:	2575-8454
Online-Zugang:	Volltext
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