No-regret learning in convex games

Proceedings of the 25th International Conference on Machine Learning

Volumn , Issue , 2008, Pages 360-367

  Gordon, Geoffrey J ^a   Greenwald, Amy ^b   Marks, Casey ^b  

^a Carnegie Mellon University ^*  (United States)

^b Brown Univerity   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONVEX OPTIMIZATION; GAME THEORY; MACHINE LEARNING; MULTI AGENT SYSTEMS; EDUCATION; LEARNING SYSTEMS; ROBOT LEARNING;

CONVEX GAME; CONVEX PROGRAMMING PROBLEMS; CORRELATED EQUILIBRIA; EXTENSIVE-FORM GAMES; MACHINE LEARNING PROBLEM; MATRIX GAME; MULTI-AGENT SETTING; NO-REGRET LEARNING; CONVEX GAMES; CORRELATED EQUILIBRIUM; MACHINE LEARNING PROBLEMS; MATRIX GAMES; MULTI-AGENT SETTINGS;

LEARNING ALGORITHMS;

EID: 56449107386     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1390156.1390202     Document Type: Conference Paper

References (16)

1. Blum, A., & Mansour, Y. (2005). From external to internal regret. Proceedings of the Conference on Computational Learning Theory (COLT).
2. Cortes, C., & Vapnik, V. N. (1995). Support-vector networks. Machine Learning Journal, 20, 273-297.
3. Forges, F., & von Stengel, B. (2002). Computationally efficient coordination in game trees (Technical Report LSE-CDAM-2002-02). London School of Economics and Political Science, Centre for Discrete and Applicable Mathematics.
4. Foster, D., & Vohra, R. (1997). Regret in the on-line decision problem. Games and Economic Behavior, 21, 40-55.
5. Gordon, G., Greenwald, A., & Marks, C. (2008). No-regret learning in convex games (Technical Report CS-08-03). Brown University, Department of Computer Science.
6. Gordon, G., Greenwald, A., Marks, C., & Zinkevich, M. (2007). No-regret learning in convex games (Technical Report CS-07-10). Brown University, Department of Computer Science.
7. Gordon, G. J. (1999a). Approximate solutions to Markov decision processes. Doctoral dissertation, Carnegie Mellon University.
8. Gordon, G. J. (1999b). Regret bounds for prediction problems. Proceedings of the ACM Conference on Computational Learning Theory.
9. Gordon, G. J. (2006). No-regret algorithms for online convex programs. Advances in Neural Information Processing Systems (NIPS), 19.
10. Greenwald, A., & Jafari, A. (2003). A general class of no-regret algorithms and game-theoretic equilibria. Proceedings of the 2003 Computational Learning Theory Conference (pp. 1-11).
11. Hazan, E., & Kale, S. (2007), Computational equivalence of fixed points and no regret algorithms, and convergence to equilibria. Advances in Neural Information Processing Systems (NIPS), 20.
12. Kalai, A., & Vempala, S. (2003). Efficient algorithms for online decision problems. Proceedings of the 16th Annual Conference on Learning Theory.
13. Marks, C. (2008). No-regret learning and game-theoretic equilibria. Ph.D. Dissertation, Department of Computer Science, Brown University, Providence, RI.
14. Shalev-Shwartz, S., & Singer, Y. (2006). Convex repeated games and Fenchel duality. Advances in Neural Information Processing Systems (NIPS), 19.
15. Stoltz, G., & Lugosi, G. (2007). Learning correlated equilibria in games with compact sets of strategies. Games and Economic Behavior, 59, 187-208.
16. Zinkevich, M. (2003). Online convex programming and generalized infinitesimal gradient ascent. Proceedings of the 20th International Conference on Machine Learning.