Open Access
| Issue |
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
|
|
|---|---|---|
| Article Number | 03027 | |
| Number of page(s) | 9 | |
| Section | Intelligent Systems and Computing in Industry, Robotics, and Smart Infrastructure | |
| DOI | https://doi.org/10.1051/itmconf/20257803027 | |
| Published online | 08 September 2025 | |
- Wang, X., Ye, J., Lui, J.C.S.: 'Decentralized Task Offloading in Edge Computing: A Multi-User Multi-Armed Bandit Approach'. Proc. IEEE Conf. Computer Communications (INFOCOM), Denver, CO, USA, May 2022, pp. 1537–1546 [Google Scholar]
- Lattimore, T., Szepesvári, C.: 'Bandit Algorithms' (Cambridge University Press, Cambridge, UK, 2020) [Google Scholar]
- Auer, P., Cesa-Bianchi, N., Fischer, P.: 'Finite-Time Analysis of the Multi-Armed Bandit Problem', Machine Learning, 2002, 47, (2-3), pp. 235–256 [Google Scholar]
- Agrawal, S., Goyal, N.: 'Analysis of Thompson Sampling for the Multi-Armed Bandit Problem'. Proc. 25th Conf. Learning Theory (COLT), Edinburgh, UK, June 2012, pp. 39-1–39-26 [Google Scholar]
- Liu, L.T., Mania, H., Jordan, M.I.: 'Competing Bandits in Matching Markets'. Proc. 23rd Int. Conf. Artificial Intelligence and Statistics (AISTATS), Palermo, Italy, Aug. 2020, pp. 1618–1628 [Google Scholar]
- Boursier, E., Perchet, V.: 'A Survey on Multi-Player Bandits', Journal of Machine Learning Research, 2024, 25, pp. 1–45 [Google Scholar]
- Xu, M., Klabjan, D.: 'Decentralized Randomly Distributed Multi-Agent Multi-Armed Bandit with Heterogeneous Rewards'. Advances in Neural Information Processing Systems 36 (NeurIPS), New Orleans, LA, USA, Dec. 2023, pp. 36701–36713 [Google Scholar]
- Fan, J., Wang, Z., Li, S., et al.: 'Multi-Player Multi-Armed Bandits with Delayed Feedback'. Proc. 12th Int. Conf. Learning Representations (ICLR), Addis Ababa, Ethiopia, May 2024, pp. 1–21 [Google Scholar]
- Kim, T., Zuo, J., Zhang, X., et al.: 'Edge-MSL: Split Learning on the Mobile Edge via Multi-Armed Bandits'. Proc. IEEE Conf. Computer Communications (INFOCOM), Vancouver, BC, Canada, May 2024, pp. 1234–1243 [Google Scholar]
- Wu, X., Li, B.: 'Achieving Regular and Fair Learning in Combinatorial Multi-Armed Bandit'. Proc. IEEE Conf. Computer Communications (INFOCOM), Vancouver, BC, Canada, May 2024, pp. 2475–2484 [Google Scholar]
- Shi, C., Shen, C.: 'Federated Multi-Armed Bandits'. Proc. 35th AAAI Conf. Artificial Intelligence (AAAI-21), Virtual Event, Feb. 2021, pp. 9066–9074 [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.