Open Access
Issue
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
Article Number 03021
Number of page(s) 13
Section Intelligent Systems and Computing in Industry, Robotics, and Smart Infrastructure
DOI https://doi.org/10.1051/itmconf/20257803021
Published online 08 September 2025
  1. Lai T.L., Robbins H.: 'Asymptotically efficient adaptive allocation rules', Advances in Applied Mathematics, 1985, 6, (1), pp. 4–22 [Google Scholar]
  2. Auer P., Cesa-Bianchi N., Fischer P.: 'Finite-time analysis of the multiarmed bandit problem', Machine Learning, 2002, 47, (2-3), pp. 235–256 [Google Scholar]
  3. Chapelle O., Li L.: 'An empirical evaluation of thompson sampling', Advances in Neural Information Processing Systems, 2011, 24, pp. 2249–2257 [Google Scholar]
  4. Seldin Y., Lugosi G.: 'One practical algorithm for both stochastic and adversarial bandits'. Proc. Int. Conf. Machine Learning, 2017, pp. 1287–1295 [Google Scholar]
  5. Silva A., Machado M.C., Dusparic E.M.: 'Cold-start reinforcement learning with Bayesian optimization'. Proc. AAAI Conf. Artificial Intelligence, 2023, 37, pp. 9340–9348 [Google Scholar]
  6. Auer P., et al.: 'The nonstochastic multiarmed bandit problem', SIAM Journal on Computing, 2002, 32, (1), pp. 48–77 [Google Scholar]
  7. Bubeck S., Cesa-Bianchi N.: 'Regret analysis of stochastic and nonstochastic multi-armed bandit problems', Foundations and Trends® in Machine Learning, 2012, 5, (1), pp. 1–122 [Google Scholar]
  8. Agarwal A., et al.: 'Corralling a band of bandit algorithms'. Proc. Conf. Learning Theory, 2017, pp. 12–38 [Google Scholar]
  9. Kaufmann E., Cappé O., Garivier A.: 'On Bayesian upper confidence bounds for bandit problems'. Proc. Artificial Intelligence and Statistics, 2012, pp. 592–600 [Google Scholar]
  10. Lattimore T., et al.: 'UCB and Thompson sampling are (almost) equivalent'. Proc. Conf. Learning Theory, 2020, pp. 3199–3218 [Google Scholar]
  11. Russo D., Van Roy B.: 'Learning to optimize via information-directed sampling', Advances in Neural Information Processing Systems, 2014, 27, pp. 1583–1591 [Google Scholar]
  12. Pacchiano A., et al.: 'Corralling stochastic bandit algorithms'. Proc. Int. Conf. Artificial Intelligence and Statistics, 2021, pp. 2116–2126 [Google Scholar]
  13. Yang Z., Xu C., Wu W., Li Z.: 'Read, attend and comment: A deep architecture for automatic news comment generation'. Proc. Conf. Empirical Methods in Natural Language Processing and Int. Joint Conf. Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, 2019, pp. 5077–5089 [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.