PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Regret Minimization and Job Scheduling
Yishay Mansour
In: SOFSEM 2010, Jan. 2010, CzechRepublic.

Abstract

Regret minimization has proven to be a very powerful tool in both computational learning theory and online algorithms. Regret minimization algorithms can guarantee, for a single decision maker, a near optimal behavior under fairly adversarial assumptions. I will discuss a recent extensions of the classical regret minimization model, which enable to handle many different settings related to job scheduling, and guarantee the near optimal online behavior.

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EPrint Type:Conference or Workshop Item (Invited Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:5955
Deposited By:Yishay Mansour
Deposited On:08 March 2010