TY - GEN
T1 - Regret minimization and job scheduling
AU - Mansour, Yishay
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77249179224&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-11266-9_6
DO - 10.1007/978-3-642-11266-9_6
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AN - SCOPUS:77249179224
SN - 3642050050
SN - 9783642050053
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 71
EP - 76
BT - SOFSEM 2010
T2 - 36th Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2010
Y2 - 23 January 2010 through 29 January 2010
ER -