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Regret Minimization and Job Scheduling

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Book cover SOFSEM 2010: Theory and Practice of Computer Science (SOFSEM 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5901))

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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References

  1. Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-Time Analysis of the Multi-Armed Bandit Problem. Machine Learning 47(2-3), 235–256 (2002)

    Article  MATH  Google Scholar 

  2. Auer, P., Cesa-Bianchi, N., Freund, Y., Schapire, R.E.: The Nonstochastic Multiarmed Bandit Problem. SIAM Journal on Computing 32(1), 48–77 (2002); (A preliminary version appeared in FOCS 1995 as Gambling in a Rigged Casino: The Adversarial Multi-Armed Bandit Problem)

    Google Scholar 

  3. Awerbuch, B., Kleinberg, R.: Online Linear Optimization and Adaptive Routing. J. Comput. Syst. Sci. 74(1), 97–114 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  4. Awerbuch, B., Mansour, Y.: Adapting to a Reliable Network Path. In: PODC, pp. 360–367 (2003)

    Google Scholar 

  5. Blum, A., Mansour, Y.: From External to Internal Regret. Journal of Machine Learning Research 8, 1307–1324 (2007)

    MathSciNet  Google Scholar 

  6. Blum, A., Burch, C.: On-Line Learning and the Metrical Task System Problem. In: COLT, pp. 45–53 (1997)

    Google Scholar 

  7. Blum, A., Burch, C., Kalai, A.: Finely-Competitive Paging. In: FOCS, pp. 450–458 (1999)

    Google Scholar 

  8. Blum, A., Chawla, S., Kalai, A.: Static Optimality and Dynamic Search-Optimality in Lists and Trees. Algorithmica 36(3), 249–260 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  9. Borodin, A., El-Yaniv, R.: Online Computation and Competitive Analysis. Cambridge University Press, Cambridge (1998)

    MATH  Google Scholar 

  10. Borodin, A., El-Yaniv, R.: Online Computation and Competitive Analysis. Cambridge University Press, Cambridge (1998)

    MATH  Google Scholar 

  11. Cesa-Bianchi, N., Lugosi, G.: Prediction, Learning and Games. Cambridge University Press, Cambridge (2006)

    MATH  Google Scholar 

  12. Cesa-Bianchi, N., Lugosi, G.: Prediction, Learning, and Games. Cambridge University Press, New York (2006)

    MATH  Google Scholar 

  13. Cesa-Bianchi, N., Freund, Y., Helmbold, D.P., Haussler, D., Schapire, R.E., Warmuth, M.K.: How to Use Expert Advice. Journal of the ACM 44(3), 427–485 (1997); (A preliminary version appeared in STOC 1993)

    Google Scholar 

  14. Cesa-Bianchi, N., Lugosi, G.: Potential-Based Algorithms in On-Line Prediction and Game Theory. Machine Learning 51(3), 239–261 (2003)

    Article  MATH  Google Scholar 

  15. Even-Dar, E., Kleinberg, R., Mannor, S., Mansour, Y.: Online Learning for Global Cost Functions. In: COLT (2009)

    Google Scholar 

  16. Foster, D., Vohra, R.: Regret in the On-Line Decision Problem. Games and Economic Behavior 21, 40–55 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  17. Foster, D.P., Vohra, R.V.: A Randomization Rule for Selecting Forecasts. Operations Research 41(4), 704–709 (1993)

    Article  MATH  Google Scholar 

  18. Freund, Y., Schapire, R.E., Singer, Y., Warmuth, M.K.: Using and Combining Predictors that Specialize. In: STOC, pp. 334–343 (1997)

    Google Scholar 

  19. Freund, Y., Schapire, R.E.: A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. In: Euro-COLT, pp. 23–37. Springer, Heidelberg (1995)

    Google Scholar 

  20. Hannan, J.: Approximation to Bayes Risk in Repeated Plays. In: Dresher, M., Tucker, A., Wolfe, P. (eds.) Contributions to the Theory of Games, vol. 3, pp. 97–139. Princeton University Press, Princeton (1957)

    Google Scholar 

  21. Hart, S., Mas-Colell, A.: A Simple Adaptive Procedure Leading to Correlated Equilibrium. Econometrica 68, 1127–1150 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  22. Kakade, S.M., Tauman-Kalai, A., Ligett, K.: Playing Games with Approximation Algorithms. In: STOC, pp. 546–555 (2007)

    Google Scholar 

  23. Kalai, A., Vempala, S.: Efficient Algorithms for Online Decision Problems. Journal of Computer and System Sciences 71(3), 291–307 (2005); An earlier version appeared in COLT (2003)

    Google Scholar 

  24. Kalai, A., Vempala, S.: Efficient Algorithms for On-Line Optimization. J. of Computer Systems and Science (JCSS) 71(3), 291–307 (2005); (A preliminary version appeared in COLT 2003)

    Google Scholar 

  25. Lai, T.L., Robbins, H.: Asymptotically Efficient Adaptive Allocations Rules. Advances in Applied Mathmatics 6, 4–22 (1985)

    Article  MATH  MathSciNet  Google Scholar 

  26. Lehrer, E.: A Wide Range No-Regret Theorem. Games and Economic Behavior 42, 101–115 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  27. Littlestone, N., Warmuth, M.K.: The Weighted Majority Algorithm. Information and Computation 108, 212–261 (1994)

    Article  MATH  MathSciNet  Google Scholar 

  28. Robbins, H.: Some Aspects of the Sequential Design of Experiments. Bulletin of the American Mathematical Society 58, 527–535 (1952)

    Article  MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel

    Yishay Mansour

Authors
  1. Yishay Mansour
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Editor information

Editors and Affiliations

  1. Department of Information and Computing Sciences, Utrecht University, Padualaan 14, 3584 CH, Utrecht, The Netherlands

    Jan van Leeuwen

  2. LaBRI, Universit Bordeaux, 351 cours de la Libration, 1F-33405, Talence Cedex, France

    Anca Muscholl

  3. Department of Computer Science & Applied Mathematics, Weizmann Institute, Faculty of Mathematics and Computer Science, 76100, Rehovot, Israel

    David Peleg

  4. Department of Software Engineering Faculty of Mathematics and Physics Malostranské nám, Charles University, 25 11800, Prague 1, Czech Republic

    Jaroslav Pokorný

  5. Software Engineering, Department of Computer Science, RWTH Aachen University, 3, Ahornstrae 55, D-52074, Aachen, Germany

    Bernhard Rumpe

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© 2010 Springer-Verlag Berlin Heidelberg

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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

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11265-2

  • Online ISBN: 978-3-642-11266-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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