Regret Minimization and Job Scheduling
In: SOFSEM 2010, Jan. 2010, CzechRepublic.
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
|EPrint Type:||Conference or Workshop Item (Invited Talk)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Subjects:||Theory & Algorithms|
|Deposited By:||Yishay Mansour|
|Deposited On:||08 March 2010|