Optimal algorithms for online convex optimization with multi-point bandit feedback

COLT 2010 - The 23rd Conference on Learning Theory

Volumn , Issue , 2010, Pages 28-40

  Agarwal, Alekh ^a   Dekel, Ofer ^b   Xiao, Lin ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BANDIT FEEDBACKS; FULL INFORMATIONS; MINIMAX REGRET; MULTIPLE POINTS; ONLINE CONVEX OPTIMIZATIONS; OPTIMAL ALGORITHM; PARTIAL INFORMATION; SMOOTH FUNCTIONS;

CONVEX OPTIMIZATION;

EID: 84860610530     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Abernethy, J., Agarwal, A., Rakhlin, A., & Bartlett, P. L. (2009). A stochastic view of optimal regret through minimax duality. Proceedings of the 22nd Annual Conference on Learning Theory.
2. Abernethy, J., & Rakhlin, A. (2009). Beating the adaptive bandit with high probability. Proceedings of the 22nd Annual Conference on Learning Theory.
3. Auer, P., Cesa-Bianchi, N., Freund, Y., & Schapire, R. E. (2003). The nonstochastic multiarmed bandit problem. SIAM J. Comput., 32, 48-77.
4. Bartlett, P. L., Dani, V., Hayes, T., Kakade, S., Rakhlin, A., & Tewari, A. (2008a). High-probability regret bounds for bandit online linear optimization. Proc. of the 21st Annual COLT.
5. Bartlett, P. L., Hazan, E., & Rakhlin, A. (2008b). Adaptive online gradient descent. Advances in Neural Information Processing Systems 20. Cambridge, MA: MIT Press.
6. Cesa-Bianchi, N., & Lugosi, G. (2006). Prediction, learning and games. Cambridge University Press.
7. Conn, A. R., Scheinberg, K., & Vicente, L. N. (2009). Introduction to derivative-free optimization. Philadelphia, PA: SIAM.
8. Dani, V., & Hayes, T. P. (2006). Robbing the bandit: Less regret in online geometric optimization against an adaptive adversary. ACM-SIAM Symposium on Discrete Algorithms.
9. Dani, V., and Hayes., T. P., & Kakade, S. M. (2008). Stochastic linear optimization under bandit feedback. Proceedings of the 21st Annual Conference on Learning Theory.
10. Flaxman, A. D., and Kalai., A. T., & Mcmahan, B. H. (2005). Online convex optimization in the bandit setting: gradient descent without a gradient. Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms (pp. 385-394).
11. Hazan, E., Agarwal, A., & Kale, S. (2007). Logarithmic regret algorithms for online convex optimization. Machine Learning, 69, 169-192.
12. Kakade, S. M., & Tewari, A. (2009). On the generalization ability of online strongly convex programming algorithms. Advances in Neural Information Processing Systems 21 (pp. 801-808).
13. Polyak, B. T., & Tsypkin, Y. Z. (1973). Pseudogradient adaption and training algorithms. Automation and Remote Control, 34, 377-397.
14. 1 regularized loss minimization. Proceedings of the 26th International Conference on Machine Learning.
15. Spall, J. C. (2003). Introduction to stochastic search and optimization: Estimation, simulation, and control. Hoboken, NJ: John Wiley and Sons.
16. Tseng, P., & Yun, S. (2009). A block-coordinate gradient descent method for linearly constrained nonsmooth separable optimization. J. of Optimization Theory and Applications, 140, 513-535.
17. Zinkevich, M. (2003). Online convex programming and generalized infinitesimal gradient ascent. Proceedings of the 20th International Conference on Machine Learning (pp. 928-936).