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. © 2010 Springer-Verlag Berlin Heidelberg.
Cite
CITATION STYLE
Mansour, Y. (2010). Regret minimization and job scheduling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5901 LNCS, pp. 71–76). https://doi.org/10.1007/978-3-642-11266-9_6
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