Routine Bandits: Minimizing Regret on Recurring Problems

Author:

Saber Hassan,Saci Léo,Maillard Odalric-Ambrym,Durand Audrey

Publisher

Springer International Publishing

Reference24 articles.

1. Abbasi-Yadkori, Y., Pál, D., Szepesvári, C.: Improved algorithms for linear stochastic bandits. In: Advances in Neural Information Processing Systems, pp. 2312–2320 (2011)

2. Agrawal, R., Teneketzis, D., Anantharam, V.: Asymptotically efficient adaptive allocation schemes for controlled IID processes: finite parameter space. IEEE Trans. Autom. Control 34(3) (1989)

3. Anandkumar, A., Ge, R., Hsu, D., Kakade, S.: A tensor spectral approach to learning mixed membership community models. In: Conference on Learning Theory, pp. 867–881. PMLR (2013)

4. Anandkumar, A., Ge, R., Hsu, D.J., Kakade, S.M., Telgarsky, M.: Tensor decompositions for learning latent variable models. J. Mach. Learn. Res. 15(1), 2773–2832 (2014)

5. Bubeck, S., Cesa-Bianchi, N.: Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Foundations and Trends in Machine Learning abs/1204.5721 (2012). http://arxiv.org/abs/1204.5721

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