On Monte-Carlo methods in convex stochastic optimization

Author:

Bartl Daniel1,Mendelson Shahar2

Affiliation:

1. Faculty of Mathematics, University of Vienna

2. Centre for Mathematics and its Applications, Australian National University Canberra

Publisher

Institute of Mathematical Statistics

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Reference48 articles.

1. BANHOLZER, D., FLIEGE, J. and WERNER, R. (2022). On rates of convergence for sample average approximations in the almost sure sense and in mean. Math. Program. 191 307–345.

2. BARTL, D. and TANGPI, L. (2020). Non-asymptotic rates for the estimation of risk measures. Preprint. Available at arXiv:2003.10479.

3. BERTSIMAS, D., GUPTA, V. and KALLUS, N. (2018). Robust sample average approximation. Math. Program. 171 217–282.

4. CHERAPANAMJERI, Y., FLAMMARION, N. and BARTLETT, P. (2019). Fast mean estimation with sub-Gaussian rates. In Conference on Learning Theory, PMLR 786–806.

5. FÖLLMER, H. and SCHIED, A. (2011). Stochastic Finance: An Introduction in Discrete Time. de Gruyter, Berlin.

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