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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Keywords
- Online Algorithm
- Comparison Class
- Cumulative Loss
- Computational Learning Theory
- Online Learning Algorithm
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Mansour, Y. (2010). Regret Minimization and Job Scheduling. In: van Leeuwen, J., Muscholl, A., Peleg, D., Pokorný, J., Rumpe, B. (eds) SOFSEM 2010: Theory and Practice of Computer Science. SOFSEM 2010. Lecture Notes in Computer Science, vol 5901. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11266-9_6
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DOI: https://doi.org/10.1007/978-3-642-11266-9_6
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