Open Access
| Issue |
ITM Web Conf.
Volume 73, 2025
International Workshop on Advanced Applications of Deep Learning in Image Processing (IWADI 2024)
|
|
|---|---|---|
| Article Number | 01026 | |
| Number of page(s) | 11 | |
| Section | Reinforcement Learning and Optimization Techniques | |
| DOI | https://doi.org/10.1051/itmconf/20257301026 | |
| Published online | 17 February 2025 | |
- R. Degenne, V. Perchet, Anytime optimal algorithms in stochastic multi-armed bandits. In Int. Conf. Mach. Learn., pp. 1587-1595, PMLR (2016). [Google Scholar]
- S. Vaswani, B. Kveton, Z. Wen, M. Ghavamzadeh, L.V.S. Lakshmanan, M. Schmidt, Model-independent online learning for influence maximization. In Int. Conf. Mach. Learn., pp. 3530-3539, PMLR (2017). [Google Scholar]
- Y. Zhou, X. Chen, J. Li, Optimal PAC multiple arm identification with applications to crowdsourcing. In Int. Conf. Mach. Learn., pp. 217-225, PMLR (2014). [Google Scholar]
- T. Lattimore, C. Szepesvári, Bandit algorithms, Cambridge University Press (2020). [CrossRef] [Google Scholar]
- Q. Hu, Performance comparison and analysis of UCB, ETC, and Thompson sampling algorithms in the multi-armed bandit problem. Highlights Sci. Eng. Technol. 94, 273-278 (2024). [Google Scholar]
- A. Carpentier, A. Lazaric, M. Ghavamzadeh, R. Munos, P. Auer, Upper-confidence- bound algorithms for active learning in multi-armed bandits. In Int. Conf. Algorithmic Learn. Theory, pp. 189-203, Springer, Berlin, Heidelberg (2011). [Google Scholar]
- C. Qu, Enhancing UCB-tuned and asymptotically optimal UCB algorithms through weighted average techniques in multi-armed bandit scenarios. Highlights Sci. Eng. Technol. 94, 187-194 (2024). [CrossRef] [Google Scholar]
- D.J. Russo, B. Van Roy, A. Kazerouni, I. Osband, Z. Wen, A tutorial on Thompson sampling. Found. Trends Mach. Learn. 11, 1-96 (2018). [CrossRef] [Google Scholar]
- F.B. Hildebrand, Introduction to numerical analysis, Courier Corporation (1987). [Google Scholar]
- A. Agresti, Categorical data analysis, John Wiley & Sons (2012). [Google Scholar]
- Y. Saito, S. Aihara, M. Matsutani, Y. Narita, Open bandit dataset and pipeline: Towards realistic and reproducible off-policy evaluation. arXiv preprint arXiv:2008.07146 (2020). [Google Scholar]
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