Regret Minimization and Job Scheduling

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. 1 Regret Minimization Consider a single decision maker attempting to optimize it performance in face of an uncertain environment. This simple online setting has attracted attention from multiple disciplines, including operations research, game theory, and computer science. In computer science, computational learning theory and online algorithms both focus on this task from different perspectives. I will concentrate only on a certain facet of this general issue of decision making, and consider settings related to regret minimization, wh...

• Type: Conference
• Year: 2010
• Where: SOFSEM
• Authors: Yishay Mansour
• Keywords: Online Regret Minimization, Regret Minimization, Regret Minimization Model, SOFSEM 2010, Theoretical Computer Science