Open Access
| Issue |
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
|
|
|---|---|---|
| Article Number | 01024 | |
| Number of page(s) | 9 | |
| Section | Deep Learning and Reinforcement Learning – Theories and Applications | |
| DOI | https://doi.org/10.1051/itmconf/20257801024 | |
| Published online | 08 September 2025 | |
- Cao, Q.: 'Exploration vs. Exploitation: Comparative Analysis and Practical Applications of Multi-Armed Bandit Algorithms', Proc. 1st Int. Conf. Engineering Management, Information Technology and Intelligence, 2024, pp.412–416. [Google Scholar]
- Liu, Z.: 'Investigation of progress and application related to Multi-Armed Bandit algorithms', Proc. Int. Conf. Machine Learning and Automation, October 2023, pp.155–159. [Google Scholar]
- Ershov, M. A., Voroshilov, A. S.: 'UCB Strategy for Gaussian and Bernoulli Multi-armed Bandits', Proc. Int. Conf. Mathematical Optimization Theory and Operations Research, September 2023, pp.67–78. [Google Scholar]
- Han, Y.: 'Comparative Evaluation, Challenges, and Diverse Applications of Multi-Armed Bandit Algorithms', Proc. Int. Conf. Highlights in Science, Engineering and Technology, January 2024, pp.94. [Google Scholar]
- Song, R.: 'Optimizing decision-making in uncertain environments through analysis of stochastic stationary Multi-Armed Bandit algorithms', Proc. 6th Int. Conf. Computing and Data Science, September 2024, 93–113. [Google Scholar]
- Zhao, J.: 'Comparison of Multi-Armed Bandit Algorithms in Advertising Recommendation Systems'. Proc. CONF-MLA 2024 Workshop: Semantic Communication Based Complexity Scalable Image Transmission System for Resource Constrained Devices, November 2024, pp.62–71. [Google Scholar]
- Umami, I., Rahmawati, L.: 'Comparing Epsilon Greedy and Thompson Sampling model for Multi-Armed Bandit algorithm on marketing dataset', J. Adv. Data Sci., 2023, 2, (2), pp.14–26. [Google Scholar]
- Fu, L.: 'Exploring the efficacy of Multi-Armed Bandit Algorithms in dynamic decision-making', Proc. 2nd Int. Conf. Machine Learning and Automation, November 2024, pp.141–148. [Google Scholar]
- Gangan, E.: 'Survey of multiarmed bandit algorithms applied to recommendation systems', Int. J. Open Inf. Technol., 2021, 9, (4), pp. 123–135. [Google Scholar]
- Yu, J.: 'Thompson -Greedy Algorithm: An Improvement to the Regret of Thompson Sampling and -Greedy on Multi-Armed Bandit Problems', Proc. Int. Conf. Software Engineering and Machine Learning, August 2023, pp.507–516. [Google Scholar]
- Jin, T., Yang, X., Xiao, X., Xu, P.: 'Thompson Sampling with Less Exploration is Fast and Optimal', Proc. 40th Int. Conf. Machine Learning, 2023, pp.15239–15261. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

