Online learning under delayed feedback

30th International Conference on Machine Learning, ICML 2013

Volumn , Issue PART 3, 2013, Pages 2503-2511

  Joulani, Pooria ^a   György, András ^a   Szepesvari, Csaba ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER AIDED INSTRUCTION; LEARNING SYSTEMS;

DELAYED FEEDBACK; EFFECT OF DELAYS; LOWER COMPLEXITY; META-ALGORITHMS; ONLINE LEARNING ALGORITHMS; STOCHASTIC PROBLEMS; SYSTEMATIC STUDY; WEB BASED LEARNING;

E-LEARNING;

EID: 84897506818     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. Agarwal, Alekh and Duchi, John. Distributed delayed stochastic optimization. In Shawe-Taylor, J., Zemel, R.S., Bartlett, P., Pereira, F., and Weinberger, K.Q. (eds.), Advances in Neural Information Processing Systems 24 (NIPS), pp. 873-881, 2011.
2. Auer, Peter, Cesa-Bianchi, Nicolò, and Fischer, Paul. Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2-3):235-256, May 2002.
3. Cesa-Bianchi, Nicolò and Lugosi, Gábor. Prediction, Learning, and Games. Cambridge University Press, New York, NY, USA, 2006. ISBN 0521841089.
4. Desautels, Thomas, Krause, Andreas, and Burdick, Joel. Parallelizing exploration-exploitation trade-offs with gaussian process bandit optimization. In Proceedings of the 29th International Conference on Machine Learning (ICML), Edinburgh, Scotland, UK, 2012. Omnipress.
5. Dudik, Miroslav, Hsu, Daniel, Kale, Satyen, Karampatziakis, Nikos, Langford, John, Reyzin, Lev, and Zhang, Tong. Efficient optimal learning for contextual bandits. In Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 169-178, Corvallis, Oregon, 2011. AUAI Press.
6. Garivier, Aurélien and Cappe, Olivier. The KL-UCB algorithm for bounded stochastic bandits and beyond. In Proceedings of the 24th Annual Conference on Learning Theory (COLT), volume 19, pp. 359-376, Budapest, Hungary, July 2011.
7. Joulani, Pooria, György, András, and Szepesvári, Csaba. Online learning under delayed feedback. Extended version of a paper submitted to ICML-2013, 2013. URL http://webdocs.cs.ualberta.ca/~pooria/ publications/DelayedFeedback-ICML2013-Extended.pdf.
8. Langford, John, Smola, Alexander, and Zinkevich, Martin. Slow learners are fast. In Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C. K. I., and Culotta, A. (eds.), Advances in Neural Information Processing Systems 22, pp. 2331-2339. 2009.
9. Li, Lihong, Chu, Wei, Langford, John, and Schapire, Robert E. A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th International Conference on World Wide Web (WWW), pp. 661-670, New York, NY, USA, 2010. ACM.
10. Mesterharm, Chris J. On-line learning with delayed label feedback. In Jain, Sanjay, Simon, HansUlrich, and Tomita, Etsuji (eds.), Algorithmic Learning Theory, volume 3734 of Lecture Notes in Computer Science, pp. 399-413. Springer Berlin Heidelberg, 2005.
11. Mesterharm, Chris J. Improving on-line learning. PhD thesis, Department of Computer Science, Rutgers University, New Brunswick, NJ, 2007.
12. Neu, Gergely, György, András, Szepesvári, Csaba, and Antos, Andras. Online markov decision processes under bandit feedback. In Lafferty, J., Williams, C. K. I., Shawe-Taylor, J., Zemel, R.S., and Culotta, A. (eds.), Advances in Neural Information Processing Systems 23 (NIPS), pp. 1804-1812, 2010.
13. Weinberger, Marcelo J. and Ordentlich, Erik. On delayed prediction of individual sequences. IEEE Transactions on Information Theory, 48 (7): 1959-1976, September 2002.