A Model for Optimizing Recalculation Schedules to Minimize Regret

Author:

Austhof Bethany,Reyzin Lev

Publisher

Springer Nature Switzerland

Reference16 articles.

1. Abbasi-Yadkori, Y., Pál, D., Szepesvári, C.: Improved algorithms for linear stochastic bandits. In: Shawe-Taylor, J., Zemel, R.S., Bartlett, P.L., Pereira, F.C.N., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held Granada, Spain, 12–14 December 2011, pp. 2312–2320 (2011)

2. Auer, P., Cesa-Bianchi, N., Freund, Y., Schapire, R.E.: The nonstochastic multiarmed bandit problem. SIAM J. Comput. 32(1), 48–77 (2002)

3. Bhattacharjee, R., Imola, J., Moshkovitz, M., Dasgupta, S.: Online k-means clustering on arbitrary data streams. In: Agrawal, S., Orabona, F. (eds.) International Conference on Algorithmic Learning Theory, Singapore, 20–23 February 2023. Proceedings of Machine Learning Research, vol. 201, pp. 204–236. PMLR (2023)

4. Bhattacharjee, R., Moshkovitz, M.: No-substitution k-means clustering with adversarial order. In: Feldman, V., Ligett, K., Sabato, S. (eds.) Algorithmic Learning Theory, Virtual Conference, Worldwide. Proceedings of Machine Learning Research, 16–19 March 2021, vol. 132, pp. 345–366. PMLR (2021)

5. DeGroot, M.H.: Probability and Statistics (1986)

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